PostedBy: autofaq 3.3.1 beta (Perl 5.006)
Archivename: graphics/algorithmsfaq PostingFrequency: biweekly See reader questions & answers on this topic!  Help others by sharing your knowledge Welcome to the FAQ for comp.graphics.algorithms! Thanks to all who have contributed. Corrections and contributions (to orourke@cs.smith.edu) always welcome.  This article is Copyright 2003 by Joseph O'Rourke. It may be freely redistributed in its entirety provided that this copyright notice is not removed.  Changed items this posting (): 5.04 New items this posting (+): none  History of Changes (approx. last six months):  Changes in 15 Feb 03 posting: 5.04: Fixed broken link in clipping article. [Thanks to Keith Forbes.] Changes in 1 Feb 03 posting: 0.04: Ashdown Radiosity back in print. [Thanks to Ian Ashdown.] 0.06: Update BSP FAQ links [Thanks to Ken Shoemake.] 0.07: Update CGAL links in source article. 3.11: Broken link re course based on Perlin's Noise book. [Thanks to Mikkel Gjoel.] 6.01: Add CGAL link to Voronoi source article. [Thanks to Andreas Fabri.] 7.02: All contributor email addresses removed to protect them from spam. Changes in 15 Jan 03 posting: 0.06: Query re reality.sgi.com/bspfaq/ [Thanks to <warp@subphase.de>.] 0.07: Update moved link on WINGED.ZIP. [Thanks to Ben Landon.] 0.07: Update Ferrar's ++ 3D rendering library link. [Thanks to F.Iannarilli, Jr.] 1.06: Added ref to AT&T Graphviz. [Thanks to Michael Meire.] 2.08: Fix sloan earclipping link. [Thanks to logicalink@juno.com.] 5.09: Update moved link re caustics. [Thanks to Ben Landon.] 5.18: Formula for distance between two 3D lines. [Thanks to Daniel Zwick.] 5.27: New article on transforming normals by Ken Shoemake. 6.08: Random points on sphere in terms of longitude & latitude [Thanks to Uffe Kousgaard.] Changes in 1 Jul 02 posting: 3.14: Correct GIF author info, add URL. [Thanks to Greg Roelofs.] Changes in 1 May 02 posting: 0.04: Errata for Watt & Watt book added. [Thanks to Jacob Marner.] 5.14: 3D viewing revised by Ken Shoemake. 5.23: Remove (erroneous) 3D medial axis info. 5.25: New article on quaternions by Ken Shoemake. 5.26: New article on camera aiming and quaternions by Ken Shoemake. 6.01: Add (correct) 3D medial axis info. (Thanks to Tamal Dey.) 6.09: Plucker coordinates article revised by Ken Shoemake. Changes in 15 Apr 02 posting: 3.05: Scaling bitmaps revised by Ken Shoemake. 3.09: Morphing article written by Ken Shoemake. 6.08: Added references on random points on a sphere (Ken Shoemake). Changes in 1 Apr 02 posting: 1.01: 2D point rotation revised by Ken Shoemake. 1.01: 2D segment intersection revised by Ken Shoemake. 5.01: 3D point rotation revised by Ken Shoemake. 0.07: Greg Ferrar's 3D rendering library no longer available. Changes in 15 Mar 02 posting: 2.03: Reference Dan Sunday's winding number algorithm. 4.04: More detail on Beziers approximating a circle. (Thanks to William Gibbons.) 5.22: Added NASA's "Intersect" code for intersecting triangulated surfaces. 5.23: Updated Cocone software description. Changes in 15 Feb 02 posting: 5.03: Noted that SutherlandHodgman can clip against any convex polygon. (Thanks to Ben Landon.) 5.15: More links on simplifying meshes. (Thanks to Stefan Krause.) Changes in 1 Jan 02 posting: 2.03: Fixed link to Franklin's code. (Thanks to Keith M. Briggs.) 5.13: Update to SWIFT++; add Terdiman's collision lib. (Thanks to Pierre Terdiman.) Changes in 1 Nov 01 posting: 6.01,02,03: Update to Qhull 3.1 release (Thanks to Brad Barber.) Changes in 15 Sep 01 posting: 0.04: "Radiosity: A Programmer's Perspective" out of print. 0.05: CQUANT97 link no longer available; RADBIB info updated. (Thanks to Ian Ashdown for both.) 2.01: Explained indices in more efficient formula, and restored Sunday's version. (Thanks to Dan Sunday.) 4.04: Link for approximating a circle via a Bezier curve (Thanks to John McDonald, Jr.) 5.10: Add in link to Jules Bloomenthal's list of papers for algorithms that could substitute for the marching cubes algorithm. 5.11: Refer to 5.10. (Thanks to Eric Haines for both.) Changes in 1 Sep 01 posting: 2.01: Fixed indices in efficient area formula (Thanks to peter@Glaze.phys.dal.ca.) 2.03: Link to classic "Point in Polygon Strategies" article. (Thanks to Eric Haines.) 5.09: Additional references for caustics (Thanks to Lars Brinkhoff.) 5.11: New links for marching cubes patent (Thanks to John Stone.) 5.17: Stale link notice. 5.23: New Cocone link for surface reconstruction.  Table of Contents  0. General Information 0.01: Charter of comp.graphics.algorithms 0.02: Are the postings to comp.graphics.algorithms archived? 0.03: How can I get this FAQ? 0.04: What are some musthave books on graphics algorithms? 0.05: Are there any online references? 0.06: Are there other graphics related FAQs? 0.07: Where is all the source? 1. 2D Computations: Points, Segments, Circles, Etc. 1.01: How do I rotate a 2D point? 1.02: How do I find the distance from a point to a line? 1.03: How do I find intersections of 2 2D line segments? 1.04: How do I generate a circle through three points? 1.05: How can the smallest circle enclosing a set of points be found? 1.06: Where can I find graph layout algorithms? 2. 2D Polygon Computations 2.01: How do I find the area of a polygon? 2.02: How can the centroid of a polygon be computed? 2.03: How do I find if a point lies within a polygon? 2.04: How do I find the intersection of two convex polygons? 2.05: How do I do a hidden surface test (backface culling) with 2D points? 2.06: How do I find a single point inside a simple polygon? 2.07: How do I find the orientation of a simple polygon? 2.08: How can I triangulate a simple polygon? 2.09: How can I find the minimum area rectangle enclosing a set of points? 3. 2D Image/Pixel Computations 3.01: How do I rotate a bitmap? 3.02: How do I display a 24 bit image in 8 bits? 3.03: How do I fill the area of an arbitrary shape? 3.04: How do I find the 'edges' in a bitmap? 3.05: How do I enlarge/sharpen/fuzz a bitmap? 3.06: How do I map a texture on to a shape? 3.07: How do I detect a 'corner' in a collection of points? 3.08: Where do I get source to display (raster font format)? 3.09: What is morphing/how is it done? 3.10: How do I quickly draw a filled triangle? 3.11: D Noise functions and turbulence in Solid texturing. 3.12: How do I generate realistic sythetic textures? 3.13: How do I convert between color models (RGB, HLS, CMYK, CIE etc)? 3.14: How is "GIF" pronounced? 4. Curve Computations 4.01: How do I generate a Bezier curve that is parallel to another Bezier? 4.02: How do I split a Bezier at a specific value for t? 4.03: How do I find a t value at a specific point on a Bezier? 4.04: How do I fit a Bezier curve to a circle? 5. 3D computations 5.01: How do I rotate a 3D point? 5.02: What is ARCBALL and where is the source? 5.03: How do I clip a polygon against a rectangle? 5.04: How do I clip a polygon against another polygon? 5.05: How do I find the intersection of a line and a plane? 5.06: How do I determine the intersection between a ray and a triangle? 5.07: How do I determine the intersection between a ray and a sphere? 5.08: How do I find the intersection of a ray and a Bezier surface? 5.09: How do I ray trace caustics? 5.10: What is the marching cubes algorithm? 5.11: What is the status of the patent on the "marching cubes" algorithm? 5.12: How do I do a hidden surface test (backface culling) with 3D points? 5.13: Where can I find algorithms for 3D collision detection? 5.14: How do I perform basic viewing in 3D? 5.15: How do I optimize/simplify a 3D polygon mesh? 5.16: How can I perform volume rendering? 5.17: Where can I get the spline description of the famous teapot etc.? 5.18: How can the distance between two lines in space be computed? 5.19: How can I compute the volume of a polyhedron? 5.20: How can I decompose a polyhedron into convex pieces? 5.21: How can the circumsphere of a tetrahedron be computed? 5.22: How do I determine if two triangles in 3D intersect? 5.23: How can a 3D surface be reconstructed from a collection of points? 5.24: How can I find the smallest sphere enclosing a set of points in 3D? 5.25: What's the big deal with quaternions? 5.26: How can I aim a camera in a specific direction? 5.27: How can I transform normals? 6. Geometric Structures and Mathematics 6.01: Where can I get source for Voronoi/Delaunay triangulation? 6.02: Where do I get source for convex hull? 6.03: Where do I get source for halfspace intersection? 6.04: What are barycentric coordinates? 6.05: How do I generate a random point inside a triangle? 6.06: How do I evenly distribute N points on (tesselate) a sphere? 6.07: What are coordinates for the vertices of an icosohedron? 6.08: How do I generate random points on the surface of a sphere? 6.09: What are Plucker coordinates? 7. Contributors 7.01: How can you contribute to this FAQ? 7.02: Contributors. Who made this all possible. Search e.g. for "Section 6" to find that section. Search e.g. for "Subject 6.04" to find that item.  Section 0. General Information  Subject 0.01: Charter of comp.graphics.algorithms comp.graphics.algorithms is an unmoderated newsgroup intended as a forum for the discussion of the algorithms used in the process of generating computer graphics. These algorithms may be recently proposed in published journals or papers, old or previously known algorithms, or hacks used incidental to the process of computer graphics. The scope of these algorithms may range from an efficient way to multiply matrices, all the way to a global illumination method incorporating raytracing, radiosity, infinite spectrum modeling, and perhaps even mirrored balls and lime jello. It is hoped that this group will serve as a forum for programmers and researchers to exchange ideas and ask questions on recent papers or current research related to computer graphics. comp.graphics.algorithms is not:  for requests for gifs, or other pictures  for requests for image translator or processing software; see alt.binaries.pictures* FAQ alt.binaries.pictures.utilities [now degenerated to pic postings] alt.graphics.pixutils (image format translation) comp.sources.misc (image viewing source code) sci.image.processing comp.graphics.apps.softimage fj.comp.image  for requests for compression software; for these try: alt.comp.compression comp.compression comp.compression.research  specifically for game development; for this try: comp.games.development.programming.misc comp.games.development.programming.algorithms  Subject 0.02: Are the postings to comp.graphics.algorithms archived? Archives may be found at: http://www.faqs.org/  Subject 0.03: How can I get this FAQ? The FAQ is posted on the 1st and 15th of every month. The easiest way to get it is to search back in your news reader for the most recent posting, with Subject: comp.graphics.algorithms Frequently Asked Questions It is posted to comp.graphics.algorithms, and crossposted to news.answers and comp.answers. If you can't find it on your newsreader, you can look at a recent HTML version at the "official" FAQ archive site: http://www.faqs.org/ The maintainer also keeps a copy of the raw ASCII, always the latest version, accessible via http://cs.smith.edu/~orourke/FAQ.html . Finally, you can ftp the FAQ from several sites, including: ftp://rtfm.mit.edu/pub/faqs/graphics/algorithmsfaq ftp://mirror.seas.gwu.edu/pub/rtfm/comp/graphics/algorithms/ The (busy) rtfm.mit.edu site lists many alternative "mirror" sites. Also can reach the FAQ from http://www.geom.umn.edu/software/cglist/, which is worth visiting in its own right.  Subject 0.04: What are some musthave books on graphics algorithms? The keywords in brackets are used to refer to the books in later questions. They generally refer to the first author except where it is necessary to resolve ambiguity or in the case of the Gems. Basic computer graphics, rendering algorithms,  [Foley] Computer Graphics: Principles and Practice (2nd Ed.), J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, AddisonWesley 1990, ISBN 0201121107; Computer Graphics: Principles and Practice, C version J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, AddisonWesley ISBN: 0201848406, 1996, 1147 pp. [Rogers:Procedural] Procedural Elements for Computer Graphics, Second Edition David F. Rogers, WCB/McGraw Hill 1998, ISBN 0070535485 [Rogers:Mathematical] Mathematical Elements for Computer Graphics 2nd Ed., David F. Rogers and J. Alan Adams, McGraw Hill 1990, ISBN 0070535302 [Watt:3D] _3D Computer Graphics, 2nd Edition_, Alan Watt, AddisonWesley 1993, ISBN 0201631865 [Glassner:RayTracing] An Introduction to Ray Tracing, Andrew Glassner (ed.), Academic Press 1989, ISBN 0122861604 [Gems I] Graphics Gems, Andrew Glassner (ed.), Academic Press 1990, ISBN 0122861655 http://www.graphicsgems.org/ for all the Gems. [Gems II] Graphics Gems II, James Arvo (ed.), Academic Press 1991, ISBN 012644800 [Gems III] Graphics Gems III, David Kirk (ed.), Academic Press 1992, ISBN 0124096700 (with IBM disk) or 0124096719 (with Mac disk) See also "AP Professional Graphics CDROM Library," Academic Press, ISBN 012059756X, which contains Gems IIII. [Gems IV] Graphics Gems IV, Paul S. Heckbert (ed.), Academic Press 1994, ISBN 0123361559 (with IBM disk) or 0123361567 (with Mac disk) [Gems V] Graphic Gems V, Alan W. Paeth (ed.), Academic Press 1995, ISBN 0125434553 (with IBM disk) [Watt:Animation] Advanced Animation and Rendering Techniques, Alan Watt, Mark Watt, AddisonWesley 1992, ISBN 0201544121 (Unofficial) errata: http://www.rolemaker.dk/other/AART/ [Bartels] An Introduction to Splines for Use in Computer Graphics and Geometric Modeling, Richard H. Bartels, John C. Beatty, Brian A. Barsky, 1987, ISBN 0934613273 [Farin] Curves and Surfaces for Computer Aided Geometric Design: A Practical Guide, 4th Edition, Gerald E. Farin, Academic Press 1996. ISBN 0122490541. [Prusinkiewicz] The Algorithmic Beauty of Plants, Przemyslaw W. Prusinkiewicz, Aristid Lindenmayer, SpringerVerlag, 1990, ISBN 0387972978, ISBN 3540972978 [Oliver] Tricks of the Graphics Gurus, Dick Oliver, et al. (2) 3.5 PC disks included, $39.95 SAMS Publishing [Hearn] Introduction to computer graphics, Hearn & Baker [Cohen] Radiosity and Realistic Imange Sythesis, Michael F. Cohen, John R. Wallace, Academic Press Professional 1993, ISBN 0121782700 [limited reprint 1999] [Ashdown] Radiosity: A Programmer's Perspective Ian Ashdown, John Wiley & Sons 1994, ISBN 0471304441, 498 pp. Back in print, Jan 2003. See www.helios32.com. [sillion] Radiosity & Global Illumination Francois X. Sillion snd Claude Puech, Morgan Kaufmann 1994, ISBN 1558602771, 252 pp. [Ebert] Texturing and Modeling  A Procedural Approach (2nd Ed.) David S. Ebert (ed.), F. Kenton Musgrave, Darwyn Peachey, Ken Perlin, Steven Worley, Academic Press 1998, ISBN 0122287304, Includes CDROM. [Schroeder] Visualization Toolkit, 2nd Edition, The: An ObjectOriented Approach to 3D Graphics (Bk/CD) (Professional Description) William J. Schroeder, Kenneth Martin, and Bill Lorensen, PrenticeHall 1998, ISBN: 0139546944 See Subject 0.07 for source. [Anderson] PC Graphics Unleashed Scott Anderson. SAMS Publishing, ISBN 0672305704 [Ammeraal] Computer Graphics for Java Programmers, Leen Ammeraal, John Wiley 1998, ISBN 0471981427. Additional information at http://home.wxs.nl/~ammeraal/ . [Eberly] 3D Game Engine Design: A Practical Approach to RealTime Computer Graphics. David Eberly, Morgan Kaufmann/Academic Press, 2001. For image processing,  [Barnsley] Fractal Image Compression, Michael F. Barnsley and Lyman P. Hurd, AK Peters, Ltd, 1993 ISBN 1568810008 [Jain] Fundamentals of Image Processing, Anil K. Jain, PrenticeHall 1989, ISBN 0133361659 [Castleman] Digital Image Processing, Kenneth R. Castleman, PrenticeHall 1996, ISBN(Cloth): 0132114674 (Description and errata at: "http://www.phoenix.net/~castlman") [Pratt] Digital Image Processing, Second Edition, William K. Pratt, WileyInterscience 1991, ISBN 0471857661 [Gonzalez] Digital Image Processing (3rd Ed.), Rafael C. Gonzalez, Paul Wintz, AddisonWesley 1992, ISBN 0201508036 [Russ] The Image Processing Handbook (3rd Ed.), John C. Russ, CRC Press and IEEE Press 1998, ISBN 0849325323 [Russ & Russ] The Image Processing Tool Kit v. 3.0 Chris Russ and John Russ, Reindeer Games Inc. 1999, ISBN 192880800X [Wolberg] Digital Image Warping, George Wolberg, IEEE Computer Society Press Monograph 1990, ISBN 0818689447 Computational geometry  [Bowyer] A Programmer's Geometry, Adrian Bowyer, John Woodwark, Butterworths 1983, ISBN 0408012420 Pbk Out of print, but see: Introduction to Computing with Geometry, Adrian Bowyer and John Woodwark, 1993 ISBN 1874728038. Available in PDF: http://www.inge.com/pubs/index.htm [Farin & Hansford] The Geometry Toolbox for Graphics and Modeling by Gerald E. Farin, Dianne Hansford A K Peters Ltd; ISBN: 1568810741 [O'Rourke (C)] Computational Geometry in C (2nd Ed.) Joseph O'Rourke, Cambridge University Press 1998, ISBN 0521640105 Pbk, ISBN 0521649765 Hbk Additional information and code at http://cs.smith.edu/~orourke/ . [O'Rourke (A)] Art Gallery Theorems and Algorithms Joseph O'Rourke, Oxford University Press 1987, ISBN 0195039653. [Goodman & O'Rourke] Handbook of Discrete and Computational Geometry J. E. Goodman and J. O'Rourke, editors. CRC Press LLC, July 1997. ISBN:0849385245 Additional information at http://cs.smith.edu/~orourke/ . [Samet:Application] Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS, Hanan Samet, AddisonWesley, Reading, MA, 1990. ISBN 0201503000. [Samet:Design & Analysis] The Design and Analysis of Spatial Data Structures, Hanan Samet, AddisonWesley, Reading, MA, 1990. ISBN 0201502550. [Mortenson] Geometric Modeling, Michael E. Mortenson, Wiley 1985, ISBN 0471882798 [Preparata] Computational Geometry: An Introduction, Franco P. Preparata, Michael Ian Shamos, SpringerVerlag 1985, ISBN 0387961313 [Okabe] Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, A. Okabe and B. Boots and K. Sugihara, John Wiley, Chichester, England, 1992. [Overmars] Computational Geometry: Algorithms and Applications M. de Berg and M. van Kreveld and M. Overmars and O. Schwarzkopf SpringerVerlag, Berlin, 1997. [Stolfi] Oriented Projective Geometry: A Framework for Geometric Computations Academic Press, 1991. [Hodge] Methods of Algebraic Geometry, Volume 1 W.V.D. Hodge and D. Pedoe, Cambridge, 1994. ISBN 05214690074 Paperback [Tamassia et al 199?] Graph Drawing: Algorithms for the Visualization of Graphs Prentice Hall; ISBN: 0133016153 Algorithms books with chapters on computational geometry  [Cormen et al.] Introduction to Algorithms, T. H. Cormen, C. E. Leiserson, R. L. Rivest, The MIT Press, McGrawHill, 1990. [Mehlhorn] Data Structures and Algorithms, K. Mehlhorn, SpringerVerlag, 1984. [Sedgewick] R. Sedgewick, Algorithms, AddisonWesley, 1988. Solid Modelling  [Mantyla] Introduction to Solid Modeling Martti Mantyla, Computer Science Press 1988, ISBN 0716780151  Subject 0.05: Are there any online references? The computational geometry community maintains its own bibliography of publications in or closely related to that subject. Every four months, additions and corrections are solicited from users, after which the database is updated and released anew. As of 7 Nov 200, it contained 13485 bibtex entries. See Jeff Erickson's page on "Computational Geometry Bibliographies": http://compgeom.cs.uiuc.edu/~jeffe/compgeom/biblios.html#geombib The bibliography can be retrieved from: ftp://ftp.cs.usask.ca/pub/geometry/geombib.tar.gz  bibliography proper ftp://ftp.cs.usask.ca/pub/geometry/ocgc19.ps.gz  overview published in '93 in SIGACT News and the Internat. J. Comput. Geom. Appl. ftp://ftp.cs.usask.ca/pub/geometry/ftphints  detailed retrieval info Universitat Politecnica de Catalunya maintains a search engine at: http://wwwma2.upc.es/~geomc/geombibe.html The ACM SIGGRAPH Online Bibliography Project, by Stephen Spencer (biblio@siggraph.org). The database is available for anonymous FTP from the ftp://siggraph.org/publications/ directory. Please download and examine the file READ_ME in that directory for more specific information concerning the database. 'netlib' is a useful source for algorithms, member inquiries for SIAM, and bibliographic searches. For information, send mail to netlib@ornl.gov, with "send index" in the body of the mail message. You can also find free sources for numerical computation in C via ftp://ftp.usc.edu/pub/Cnumanal/ . In particular, grab numcompfreec.gz in that directory. Check out Nick Fotis's computer graphics resources FAQ  it's packed with pointers to all sorts of great computer graphics stuff. This FAQ is posted biweekly to comp.graphics. This WWW page contains links to a large number of computer graphic related pages: http://www.dataspace.com:84/vlib/compgraphics.html There's a Computer Science Bibliography Server at: http://glimpse.cs.arizona.edu:1994/bib/ with Computer Graphics, Vision and Radiosity sections A comprehensive bibliography of color quantization papers and articles (CQUANT97) was available at http://www.ledalite.com/library/cgis.htm. [Link no longer available  replacement? JOR] Modelling physically based systems for animation: http://www.cc.gatech.edu/gvu/animation/Animation.html The University of Manchester NURBS Library: ftp://unix.hensa.ac.uk/pub/misc/unix/nurbs/ For an implementation of Seidel's algorithm for fast trapezoidation and triangulation of polygons. You can get the code from: ftp://ftp.cs.unc.edu/pub/users/narkhede/triangulation.tar.gz Ray tracing bibliography: http://www.acm.org/tog/resources/bib/ Quaternions and other comp sci curiosities: ftp://ftp.netcom.com/pub/hb/hbaker/hakmem/ Directory of Computational Geometry Software, collected by Nina Amenta (nina@cs.utexas.edu) Nina Amenta is maintaining a WWW directory to computational geometry software. The directory lives at The Geometry Center. It has pointers to lots of convex hull and voronoi diagram programs, triangulations, collision detection, polygon intersection, smallest enclosing ball of a point set and other stuff. http://www.geom.umn.edu/software/cglist/ A compact reference for realtime 3D computer graphics programming: http://www.math.mcgill.ca/~loisel/ RADBIB is a comprehensive bibliography of radiosity and related global illumination papers, articles, and books. It currently includes 1,972 references. This bibliography is available in BibTex format (with a release date of 15 Jul 01) from: http://www.helios32.com/ under "Resources." The "Electronic Visualization Library" (EVlib) is a domain secific digital library for Scientific Visualization and Computer Graphics: http://visinfo.zib.de/ 3D Object Intersection: http://www.realtimerendering.com/int/ This page presents information about a wide variety of 3D object/object intersection tests. Presented in grid form, each axis lists ray, plane, sphere, triangle, box, frustum, and other objects. For each combination (e.g. sphere/box), references to articles, books, and online resources are given. Ray Tracing News, ed. Eric Haines: http://www.raytracingnews.com .  Subject 0.06: Are there other graphics related FAQs? BSP Tree FAQ by Bretton Wade http://www.andrew.cmu.edu/~rrost/bsp/ ftp://ftp.sgi.com/other/bspfaq/faq/bspfaq.html ftp://ftp.sgi.com/other/bspfaq/faq/bspfaq.txt and see ftp://ftp.sgi.com/other/bspfaq/ Gamma and Color FAQs by Charles A. Poynton has ftp://ftp.inforamp.net/pub/users/poynton/doc/colour/ http://www.inforamp.net/~poynton/ The documents are mirrored in Darmstadt, Germany at ftp://ftp.igd.fhg.de/pub/doc/colour/  Subject 0.07: Where is all the source? Graphics Gems source code. http://www.graphicsgems.org This site is now the offical distribution site for Graphics Gems code. Master list of Computational Geometry software: http://www.geom.umn.edu/software/cglist Described in [Goodman & O'Rourke], Chap. 52. Jeff Erikson's software list: http://compgeom.cs.uiuc.edu/~jeffe/compgeom/compgeom.html Dave Eberly's extensive collection of free geometry, graphics, and image processing software: http://www.magicsoftware.com/ General 'stuff' ftp://wuarchive.wustl.edu/graphics/ There are a number of interesting items in http://graphics.lcs.mit.edu/~seth including:  Code for 2D Voronoi, Delaunay, and Convex hull  Mike Hoymeyer's implementation of Raimund Seidel's O( d! n ) time linear programming algorithm for n constraints in d dimensions  geometric models of UC Berkeley's new computer science building Sources to "Computational Geometry in C", by J. O'Rourke can be found at http://cs.smith.edu/~orourke/books/compgeom.html or ftp://cs.smith.edu/pub/compgeom . Greg Ferrar's C++ 3D rendering library is available at http://www.flowerfire.com/ferrar/Graph3D.html TAGL is a portable and extensible library that provides a subset of OpenGL functionalities. ftp://sunsite.unc.edu/pub/packages/programming/graphics/ Try ftp://x2ftp.oulu.fi for /pub/msdos/programming/docs/graphpro.lzh by Michael Abrash. His XSharp package has an implementation of Xiaoulin Wu's antialiasing algorithm (in C). Example sources for BSP tree algorithms can be found at http://reality.sgi.com/bspfaq/, item 24. Mel Slater (mel@dcs.qmw.ac.uk) also made some implementations of BSP trees and shadows for static scenes using shadow volumes code available http://www.dcs.qmw.ac.uk/~mel/BSP.html ftp://ftp.dcs.qmw.ac.uk/people/mel/BSP The Visualization Toolkit (A visualization textbook, C++ library and Tclbased interpreter) (see [Schroeder]): http://www.kitware.com/vtk.html WINGED.ZIP, a C++ implementation of Baumgart's wingededge data structure: ftp://ftp.ledalite.com/pub/ CGAL, the Computational Geometry Algorithms Library, is written in C++ and is available at http://www.cgal.org. CGAL contains algorithms and data structures for 2D computations (convex hull, Delaunay, constrained Delaunay, Voronoi diagram, regular traingulation, (weighted) Alpha shapes, polytope distance, boolean operations on polygons, decomposition of polygons in monotone or convex parts, arrangements, etc.), 3D, and arbitrary dimensions. A C++ NURBS library written by Lavoie Philippe. Version 2.1. Results may be exported as POVRay, RIB (renderman) or VRML files. It also offers wrappers to OpenGL: http://yukon.genie.uottawa.ca/~lavoie/software/nurbs/ Paul Bourke has code for several problems, including isosurface generation and Delauney triangulation, at: http://www.swin.edu.au/astronomy/pbourke/geometry/ http://www.swin.edu.au/astronomy/pbourke/modeling/ A nearly comprehensive list of available 3D engines (most with source code): http://cg.cs.tuberlin.de/~ki/engines.html See also 5.17: Where can I get the spline description of the famous teapot etc.? Interactive Geometry Software called "Cinderella": http://www.cinderella.de  Section 1. 2D Computations: Points, Segments, Circles, Etc.  Subject 1.01: How do I rotate a 2D point? In 2D, you make (X,Y) from (x,y) with a rotation by angle t so: X = x cos t  y sin t Y = x sin t + y cos t As a 2x2 matrix this is very simple. If you want to rotate a column vector v by t degrees using matrix M, use M = [cos t sin t] [sin t cos t] in the product M v. If you have a row vector, use the transpose of M (turn rows into columns and vice versa). If you want to combine rotations, in 2D you can just add their angles, but in higher dimensions you must multiply their matrices.  Subject 1.02: How do I find the distance from a point to a line? Let the point be C (Cx,Cy) and the line be AB (Ax,Ay) to (Bx,By). Let P be the point of perpendicular projection of C on AB. The parameter r, which indicates P's position along AB, is computed by the dot product of AC and AB divided by the square of the length of AB: (1) AC dot AB r =  AB^2 r has the following meaning: r=0 P = A r=1 P = B r<0 P is on the backward extension of AB r>1 P is on the forward extension of AB 0<r<1 P is interior to AB The length of a line segment in d dimensions, AB is computed by: L = sqrt( (BxAx)^2 + (ByAy)^2 + ... + (BdAd)^2) so in 2D: L = sqrt( (BxAx)^2 + (ByAy)^2 ) and the dot product of two vectors in d dimensions, U dot V is computed: D = (Ux * Vx) + (Uy * Vy) + ... + (Ud * Vd) so in 2D: D = (Ux * Vx) + (Uy * Vy) So (1) expands to: (CxAx)(BxAx) + (CyAy)(ByAy) r =  L^2 The point P can then be found: Px = Ax + r(BxAx) Py = Ay + r(ByAy) And the distance from A to P = r*L. Use another parameter s to indicate the location along PC, with the following meaning: s<0 C is left of AB s>0 C is right of AB s=0 C is on AB Compute s as follows: (AyCy)(BxAx)(AxCx)(ByAy) s =  L^2 Then the distance from C to P = s*L.  Subject 1.03: How do I find intersections of 2 2D line segments? This problem can be extremely easy or extremely difficult; it depends on your application. If all you want is the intersection point, the following should work: Let A,B,C,D be 2space position vectors. Then the directed line segments AB & CD are given by: AB=A+r(BA), r in [0,1] CD=C+s(DC), s in [0,1] If AB & CD intersect, then A+r(BA)=C+s(DC), or Ax+r(BxAx)=Cx+s(DxCx) Ay+r(ByAy)=Cy+s(DyCy) for some r,s in [0,1] Solving the above for r and s yields (AyCy)(DxCx)(AxCx)(DyCy) r =  (eqn 1) (BxAx)(DyCy)(ByAy)(DxCx) (AyCy)(BxAx)(AxCx)(ByAy) s =  (eqn 2) (BxAx)(DyCy)(ByAy)(DxCx) Let P be the position vector of the intersection point, then P=A+r(BA) or Px=Ax+r(BxAx) Py=Ay+r(ByAy) By examining the values of r & s, you can also determine some other limiting conditions: If 0<=r<=1 & 0<=s<=1, intersection exists r<0 or r>1 or s<0 or s>1 line segments do not intersect If the denominator in eqn 1 is zero, AB & CD are parallel If the numerator in eqn 1 is also zero, AB & CD are collinear. If they are collinear, then the segments may be projected to the x or yaxis, and overlap of the projected intervals checked. If the intersection point of the 2 lines are needed (lines in this context mean infinite lines) regardless whether the two line segments intersect, then If r>1, P is located on extension of AB If r<0, P is located on extension of BA If s>1, P is located on extension of CD If s<0, P is located on extension of DC Also note that the denominators of eqn 1 & 2 are identical. References: [O'Rourke (C)] pp. 24951 [Gems III] pp. 199202 "Faster Line Segment Intersection,"  Subject 1.04: How do I generate a circle through three points? Let the three given points be a, b, c. Use _0 and _1 to represent x and y coordinates. The coordinates of the center p=(p_0,p_1) of the circle determined by a, b, and c are: A = b_0  a_0; B = b_1  a_1; C = c_0  a_0; D = c_1  a_1; E = A*(a_0 + b_0) + B*(a_1 + b_1); F = C*(a_0 + c_0) + D*(a_1 + c_1); G = 2.0*(A*(c_1  b_1)B*(c_0  b_0)); p_0 = (D*E  B*F) / G; p_1 = (A*F  C*E) / G; If G is zero then the three points are collinear and no finiteradius circle through them exists. Otherwise, the radius of the circle is: r^2 = (a_0  p_0)^2 + (a_1  p_1)^2 Reference: [O' Rourke (C)] p. 201. Simplified by Jim Ward.  Subject 1.05: How can the smallest circle enclosing a set of points be found? This circle is often called the minimum spanning circle. It can be computed in O(n log n) time for n points. The center lies on the furthestpoint Voronoi diagram. Computing the diagram constrains the search for the center. Constructing the diagram can be accomplished by a 3D convex hull algorithm; for that connection, see, e.g., [O'Rourke (C), p.195ff]. For direct algorithms, see: S. Skyum, "A simple algorithm for computing the smallest enclosing circle" Inform. Process. Lett. 37 (1991) 121125. J. Rokne, "An Easy Bounding Circle" [Gems II] pp.1416.  Subject 1.06: Where can I find graph layout algorithms? See the paper by Eades and Tamassia, "Algorithms for Drawing Graphs: An Annotated Bibliography," Tech Rep CS8909, Dept. of CS, Brown Univ, Feb. 1989. A revised version of the annotated bibliography on graph drawing algorithms by Giuseppe Di Battista, Peter Eades, and Roberto Tamassia is now available from ftp://wilma.cs.brown.edu/pub/papers/compgeo/gdbiblio.tex.gz and ftp://wilma.cs.brown.edu/pub/papers/compgeo/gdbiblio.ps.gz Commercial software includes the Graph Layout Toolkit from Tom Sawyer Software http://www.tomsawyer.com and Northwoods Software's GO++ http://www.nwoods.com/go/ . Perhaps the best code is the AT&T Research Labs open C source: http://www.research.att.com/sw/tools/graphviz/  Section 2. 2D Polygon Computations  Subject 2.01: How do I find the area of a polygon? The signed area can be computed in linear time by a simple sum. The key formula is this: If the coordinates of vertex v_i are x_i and y_i, twice the signed area of a polygon is given by 2 A( P ) = sum_{i=0}^{n1} (x_i y_{i+1}  y_i x_{i+1}). Here n is the number of vertices of the polygon, and index arithmetic is mod n, so that x_n = x_0, etc. A rearrangement of terms in this equation can save multiplications and operate on coordinate differences, and so may be both faster and more accurate: 2 A(P) = sum_{i=0}^{n1} ( x_i (y_{i+1}  y_{i1}) ) Here again modular index arithmetic is implied, with n=0 and 1=n1. This can be avoided by extending the x[] and y[] arrays up to [n+1] with x[n]=x[0], y[n]=y[0] and y[n+1]=y[1], and using instead 2 A(P) = sum_{i=1}^{n} ( x_i (y_{i+1}  y_{i1}) ) References: [O' Rourke (C)] Thm. 1.3.3, p. 21; [Gems II] pp. 56: "The Area of a Simple Polygon", Jon Rokne. Dan Sunday's explanation: http://GeometryAlgorithms.com/Archive/algorithm_0101/ where To find the area of a planar polygon not in the xy plane, use: 2 A(P) = abs(N . (sum_{i=0}^{n1} (v_i x v_{i+1}))) where N is a unit vector normal to the plane. The `.' represents the dot product operator, the `x' represents the cross product operator, and abs() is the absolute value function. [Gems II] pp. 170171: "Area of Planar Polygons and Volume of Polyhedra", Ronald N. Goldman.  Subject 2.02: How can the centroid of a polygon be computed? The centroid (a.k.a. the center of mass, or center of gravity) of a polygon can be computed as the weighted sum of the centroids of a partition of the polygon into triangles. The centroid of a triangle is simply the average of its three vertices, i.e., it has coordinates (x1 + x2 + x3)/3 and (y1 + y2 + y3)/3. This suggests first triangulating the polygon, then forming a sum of the centroids of each triangle, weighted by the area of each triangle, the whole sum normalized by the total polygon area. This indeed works, but there is a simpler method: the triangulation need not be a partition, but rather can use positively and negatively oriented triangles (with positive and negative areas), as is used when computing the area of a polygon. This leads to a very simple algorithm for computing the centroid, based on a sum of triangle centroids weighted with their signed area. The triangles can be taken to be those formed by any fixed point, e.g., the vertex v0 of the polygon, and the two endpoints of consecutive edges of the polygon: (v1,v2), (v2,v3), etc. The area of a triangle with vertices a, b, c is half of this expression: (b[X]  a[X]) * (c[Y]  a[Y])  (c[X]  a[X]) * (b[Y]  a[Y]); Code available at ftp://cs.smith.edu/pub/code/centroid.c (3K). Reference: [Gems IV] pp.36; also includes code.  Subject 2.03: How do I find if a point lies within a polygon? The definitive reference is "Point in Polygon Strategies" by Eric Haines [Gems IV] pp. 2446. Now also at http://www.erichaines.com/ptinpoly. The code in the Sedgewick book Algorithms (2nd Edition, p.354) fails under certain circumstances. See http://condor.informatik.UniOldenburg.DE/~stueker/graphic/index.html for a discussion. The essence of the raycrossing method is as follows. Think of standing inside a field with a fence representing the polygon. Then walk north. If you have to jump the fence you know you are now outside the poly. If you have to cross again you know you are now inside again; i.e., if you were inside the field to start with, the total number of fence jumps you would make will be odd, whereas if you were ouside the jumps will be even. The code below is from Wm. Randolph Franklin <wrf@ecse.rpi.edu> (see URL below) with some minor modifications for speed. It returns 1 for strictly interior points, 0 for strictly exterior, and 0 or 1 for points on the boundary. The boundary behavior is complex but determined; in particular, for a partition of a region into polygons, each point is "in" exactly one polygon. (See p.243 of [O'Rourke (C)] for a discussion of boundary behavior.) int pnpoly(int npol, float *xp, float *yp, float x, float y) { int i, j, c = 0; for (i = 0, j = npol1; i < npol; j = i++) { if ((((yp[i]<=y) && (y<yp[j]))  ((yp[j]<=y) && (y<yp[i]))) && (x < (xp[j]  xp[i]) * (y  yp[i]) / (yp[j]  yp[i]) + xp[i])) c = !c; } return c; } The code may be further accelerated, at some loss in clarity, by avoiding the central computation when the inequality can be deduced, and by replacing the division by a multiplication for those processors with slow divides. For code that distinguishes strictly interior points from those on the boundary, see [O'Rourke (C)] pp. 239245. For a method based on winding number, see Dan Sunday, "Fast Winding Number Test for Point Inclusion in a Polygon," http://softsurfer.com/algorithms.htm, March 2001. References: Franklin's code: http://www.ecse.rpi.edu/Homepages/wrf/research/geom/pnpoly.html [Gems IV] pp. 2446 [O'Rourke (C)] Sec. 7.4. [Glassner:RayTracing]  Subject 2.04: How do I find the intersection of two convex polygons? Unlike intersections of general polygons, which might produce a quadratic number of pieces, intersection of convex polygons of n and m vertices always produces a polygon of at most (n+m) vertices. There are a variety of algorithms whose time complexity is proportional to this size, i.e., linear. The first, due to Shamos and Hoey, is perhaps the easiest to understand. Let the two polygons be P and Q. Draw a horizontal line through every vertex of each. This partitions each into trapezoids (with at most two triangles, one at the top and one at the bottom). Now scan down the two polygons in concert, one trapezoid at a time, and intersect the trapezoids if they overlap vertically. A more difficulttodescribe algorithm is in [O'Rourke (C)], pp. 252262. This walks around the boundaries of P and Q in concert, intersecting edges. An implementation is included in [O'Rourke (C)].  Subject 2.05: How do I do a hidden surface test (backface culling) with 2D points? c = (x1x2)*(y3y2)(y1y2)*(x3x2) x1,y1, x2,y2, x3,y3 = the first three points of the polygon. If c is positive the polygon is visible. If c is negative the polygon is invisible (or the other way).  Subject 2.06: How do I find a single point inside a simple polygon? Given a simple polygon, find some point inside it. Here is a method based on the proof that there exists an internal diagonal, in [O'Rourke (C), 1314]. The idea is that the midpoint of a diagonal is interior to the polygon. 1. Identify a convex vertex v; let its adjacent vertices be a and b. 2. For each other vertex q do: 2a. If q is inside avb, compute distance to v (orthogonal to ab). 2b. Save point q if distance is a new min. 3. If no point is inside, return midpoint of ab, or centroid of avb. 4. Else if some point inside, qv is internal: return its midpoint. Code for finding a diagonal is in [O'Rourke (C), 3539], and is part of many other software packages. See Subject 0.07: Where is all the source?  Subject 2.07: How do I find the orientation of a simple polygon? Compute the signed area (Subject 2.01). The orientation is counterclockwise if this area is positive. A slightly faster method is based on the observation that it isn't necessary to compute the area. Find the lowest vertex (or, if there is more than one vertex with the same lowest coordinate, the rightmost of those vertices) and then take the cross product of the edges fore and aft of it. Both methods are O(n) for n vertices, but it does seem a waste to add up the total area when a single cross product (of just the right edges) suffices. Code for this is available at ftp://cs.smith.edu/pub/code/polyorient.C (2K). The reason that the lowest, rightmost (or any other such extreme) point works is that the internal angle at this vertex is necessarily convex, strictly less than pi (even if there are several equallylowest points).  Subject 2.08: How can I triangulate a simple polygon? Triangulation of a polygon partitions its interior into triangles with disjoint interiors. Usually one restricts corners of the triangles to coincide with vertices of the polygon, in which case every polygon of n vertices can be triangulated, and all triangulations contain n2 triangles, employing n3 "diagonals" (chords between vertices that otherwise do not touch the boundary of the polygon). Triangulations can be constructed by a variety of algorithms, ranging from a naive search for noncrossing diagonals, which is O(n^4), to "ear" clipping, which is O(n^2) and relatively straightforward to implement [O'Rourke (C), Chap. 1], to partitioning into monotone polygons, which leads to O(n log n) time complexity [O'Rourke (C), Chap. 2; Overmars, Chap. 3], to several randomized algorithmsby Clarkson et al., by Seidel, and by Devillers, which lead to O(n log* n) complexityto Chazelle's lineartime algorithm, which has yet to be implemented. There is a tradeoff between code complexity and time complexity. Fortunately, several of the algorithms have been implemented and are available: Earclipping: http://cs.smith.edu/~orourke/books/compgeom.html ftp://ftp.cis.uab.edu/pub/sloan/Software/triangulation/src/ Seidel's Alg: http://www.cs.unc.edu/~dm/CODE/GEM/chapter.html ftp://ftp.cs.unc.edu/pub/users/narkhede/triangulation.tar.gz http://reality.sgi.com/atul/code/chapter.html See also the collection of triangulation links at http://www.geom.umn.edu/software/cglist/ References: [O'Rourke (C)] [Overmars] [Gems V] Clarkson, K., Tarjan, R., and VanWyk, C. A fast Las Vegas algorithm for triangulating a simple polygon. Discrete and Computational Geometry, 4(1):423432, 1989. Clarkson, K., Cole, R., Tarjan, R. Randomized parallel algorithms for trapezoidal diagrams. Int. J. Comp. Geom. Appl., 117133, 1992. http://cm.belllabs.com/cm/cs/who/clarkson/tri.html Seidel, R. (1991), A simple and fast incremental randomized algorithm for computing trapezoidal decompositions and for triangulating polygons, Comput. Geom. Theory Appl. 1, 5164. Devillers, O. (1992), Randomization yields simple O(n log* n) algorithms for difficult Omega(n) problems, Internat. J. Comput. Geom. Appl. 2(1), 97111. Chazelle, B. (1991), Triangulating a simple polygon in linear time, Discrete Comput. Geom. 6, 485524. Held, M. (1998) "FIST: Fast IndustrialStrength Triangulation". http://www.cosy.sbg.ac.at/~held/projects/triang/triang.html  Subject 2.09: How can I find the minimum area rectangle enclosing a set of points? First take the convex hull of the points. Let the resulting convex polygon be P. It has been known for some time that the minimum area rectangle enclosing P must have one rectangle side flush with (i.e., collinear with and overlapping) one edge of P. This geometric fact was used by Godfried Toussaint to develop the "rotating calipers" algorithm in a hardtofind 1983 paper, "Solving Geometric Problems with the Rotating Calipers" (Proc. IEEE MELECON). The algorithm rotates a surrounding rectangle from one flush edge to the next, keeping track of the minimum area for each edge. It achieves O(n) time (after hull computation). See the "Rotating Calipers Homepage" http://www.cs.mcgill.ca/~orm/rotcal.frame.html for a description and applet.  Section 3. 2D Image/Pixel Computations  Subject 3.01: How do I rotate a bitmap? The easiest way, according to the comp.graphics faq, is to take the rotation transformation and invert it. Then you just iterate over the destination image, apply this inverse transformation and find which source pixel to copy there. A much nicer way comes from the observation that the rotation matrix: R(T) = { { cos(T), sin(T) }, { sin(T), cos(T) } } is formed my multiplying three matrices, namely: R(T) = M1(T) * M2(T) * M3(T) where M1(T) = { { 1, tan(T/2) }, { 0, 1 } } M2(T) = { { 1, 0 }, { sin(T), 1 } } M3(T) = { { 1, tan(T/2) }, { 0, 1 } } Each transformation can be performed in a separate pass, and because these transformations are either rowpreserving or columnpreserving, antialiasing is quite easy. Another fast approach is to perform first a columnpreserving roation, and then a rowpreserving rotation. For an image W pixels wide and H pixels high, this requires W+H BitBlt operations in comparison to the bruteforce rotation, which uses W*H SetPixel operations (and a lot of multiplying). Reference: Paeth, A. W., "A Fast Algorithm for General Raster Rotation", Proceedings Graphics Interface '89, Canadian Information Processing Society, 1986, 7781 [Note  email copies of this paper are no longer available] [Gems I]  Subject 3.02: How do I display a 24 bit image in 8 bits? [Gems I] pp. 287293, "A Simple Method for Color Quantization: Octree Quantization" B. Kurz. Optimal Color Quantization for Color Displays. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1983, pp. 217224. [Gems II] pp. 116125, "Efficient Inverse Color Map Computation" This describes an efficient technique to map actual colors to a reduced color map, selected by some other technique described in the other papers. [Gems II] pp. 126133, "Efficient Statistical Computations for Optimal Color Quantization" Xiaolin Wu. Color Quantization by Dynamic Programming and Principal Analysis. ACM Transactions on Graphics, Vol. 11, No. 4, October 1992, pp 348372.  Subject 3.03: How do I fill the area of an arbitrary shape? "A Fast Algorithm for the Restoration of Images Based on Chain Codes Description and Its Applications", L.W. Chang & K.L. Leu, Computer Vision, Graphics, and Image Processing, vol.50, pp296307 (1990) Heckbert, Paul S., Generic Convex Polygon Scan Conversion and Clipping, Graphics Gems, p. 8486, code: p. 667680, PolyScan/. Heckbert, Paul S., Concave Polygon Scan Conversion, Graphics Gems, p. 8791, code: p. 681684, ConcaveScan.c. http://www.acm.org/tog/GraphicsGems/gems/PolyScan/ http://www.acm.org/tog/GraphicsGems/gems/ConcaveScan.c For filling a region of a bitmap, see Heckbert, Paul S., A Seed Fill Algorithm, Graphics Gems, p. 275277, code: p. 721722, SeedFill.c. The code is at http://www.acm.org/tog/GraphicsGems/gems/SeedFill.c [Gems I] [Foley] [Hearn]  Subject 3.04: How do I find the 'edges' in a bitmap? A simple method is to put the bitmap through the filter: 1 1 1 1 8 1 1 1 1 This will highlight changes in contrast. Then any part of the picture where the absolute filtered value is higher than some threshold is an "edge". A more appropriate edge detector for noisy images is described by Van Vliet et al. "A nonlinear Laplace operator as edge detector in noisy images", in Computer Vision, Graphics, and image processing 45, pp. 167195, 1989.  Subject 3.05: How do I enlarge/sharpen/fuzz a bitmap? Sharpening of bitmaps can be done by the following algorithm: I_enh(x,y) = I_fuz(x,y)k*Laplace(I_fuz(x,y)) or in words: An image can be sharpened by subtracting a positive fraction k of the Laplace from the fuzzy image. One "Laplace" kernel, approximating a Laplacian operator, is: 1 1 1 1 8 1 1 1 1 The following library implements Fast Gaussian Blurs: MAGIC: An ObjectOriented Library for Image Analysis by David Eberly The library source code and the documentation (in Latex) are at http://www.magicsoftware.com/ The code compiles on Unix systems using g++ and on PCs using Microsoft Windows 3.1 and Borland C++. The fast Gaussian blurring is based on a finite difference method for solving s u_s = s^2 \nabla^2 u where s is the standard deviation of the Gaussian (t = s^2/2). It takes advantage of geometrically increasing steps in s (rather than linearly increasing steps in t), thus getting to a larger "time" rapidly, but still retaining stability. Section 4.5 of the documentation contains the algorithm description and implementation. A bitmap is a sampled image, a special case of a digital signal, and suffers from two limitations common to all digital signals. First, it cannot provide details at fine enough spacing to exactly reproduce every continuous image, nor even more detailed sampled images. And second, each sample approximates the infinitely fine variability of ideal values with a discrete set of ranges encoded in a small number of bitssometimes just one bit per pixel. Most bitmaps have another limitation imposed: The values cannot be negative. The resolution limitation is especially important, but see "How do I display a 24 bit image in 8 bits?" for range issues. The ideal way to enlarge a bitmap is to work from the original continuous image, magnifying and resampling it. The standard way to do it in practice is to (conceptually) reconstruct a continuous image from the bitmap, and magnify and resample that instead. This will not give the same results, since details of the original have already been lost, but it is the best approach possible given an already sampled image. More details are provided below. Both sharpening and fuzzing are examples of filtering. Even more specifically, they can be both be accomplished with filters which are linear and shift invariant. A crude way to sharpen along a row (or column) is to set output pixel B[n] to the difference of input pixels, A[n]A[n1]. A similarly crude way to fuzz is to set B[n] to the average of input pixels, 1/2*A[n]+1/2*A[n1]. In each case the output is a weighted sum of input pixels, a "convolution". One important characteristic of such filters is that a sinusoid going in produces a sinusoid coming out, one of the same frequency. Thus the Fourier transform, which decomposes a signal into sinusoids of various frequencies, is the key to analysis of these filters. The simplest (and most efficient) way to handle the two dimensions of images is to operate on first the rows then the columns (or vice versa). Fourier transforms and many filters allow this separation. A filter is linear if it satisfies two simple relations between the input and output: scaling the input by a factor scales the output by the same factor, and the sum of two inputs gives the sum of the two outputs. A filter is shift invariant if shifting the input up, down, left, or right merely shifts the output the same way. When a filter is both linear and shift invariant, it can be implemented as a convolution, a weighted sum. If you find the output of the filter when the input is a single pixel with value one in a sea of zeros, you will know all the weights. This output is the impulse response of the filter. The Fourier transform of the impulse response gives the frequency response of the filter. The pattern of weights read off from the impulse response gives the filter kernel, which will usually be displayed (for image filters) as a 2D stencil array, and it is almost always symmetric around the center. For example, the following filter, approximating a Laplacian (and used for detecting edges), is centered on the negative value. 1/6 4/6 1/6 4/6 20/6 4/6 1/6 4/6 1/6 The symmetry allows a streamlined implementation. Suppose the input image is in A, and the output is to go into B. Then compute B[i][j] = (A[i1][j1]+A[i1][j+1]+A[i+1][j1]+A[i+1][j+1] +4.0*(A[i1][j]+A[i][j1]+A[i][j+1]+A[i+1][j]) 20.0*A[i][j])/6.0 Ideal blurring is uniform in all directions, in other words it has circular symmetry. Gaussian blurs are popular, but the obvious code is slow for wide blurs. A cheap alternative is the following filter (written for rows, but then applied to the columns as well). B[i][j] = ((A[i][j]*2+A[i][j1]+A[i][j+1])*4 +A[i][j1]+A[i][j+1]A[i][j3]A[i][j+3])/16 For sharpening, subtract the results from the original image, which is equivalent to using the following. B[i][j] = ((A[i][j]*2A[i][j1]A[i][j+1])*4 A[i][j1]A[i][j+1]+A[i][j3]+A[i][j+3])/16 Credit for this filter goes to Ken Turkowski and Steve Gabriel. Reconstruction is impossible without some assumptions, and because of the importance of sinusoids in filtering it is traditional to assume the continuous image is made of sinusoids mixed together. That makes more sense for sounds, where signal processing began, than it does for images, especially computer images of character shapes, sharp surface features, and halftoned shading. As pointed out above, often image values cannot be negative, unlike sinusoids. Also, real world images contain noise. The best noise suppressors (and edge detectors) are, ironically, nonlinear filters. The simplest way to double the size of an image is to use each of the original pixels twice in its row and in its column. For much better results, try this instead. Put zeros between the original pixels, then use the blurring filter given a moment ago. But you might want to divide by 8 instead of 16 (since the zeros will dim the image otherwise). To instead shrink the image by half (in both vertical and horizontal), first apply the filter (dividing by 16), then throw away every other pixel. Notice that there are obvious optimizations involving arithmetic with powers of two, zeros which are in known locations, and pixels which will be discarded.  Subject 3.06: How do I map a texture on to a shape? Paul S. Heckbert, "Survey of Texture Mapping", IEEE Computer Graphics and Applications V6, #11, Nov. 1986, pp 5667 revised from Graphics Interface '86 version Eric A. Bier and Kenneth R. Sloan, Jr., "TwoPart Texture Mappings", IEEE Computer Graphics and Applications V6 #9, Sept. 1986, pp 4053 (projection parameterizations)  Subject 3.07: How do I detect a 'corner' in a collection of points? [Currently empty entry.]  Subject 3.08: Where do I get source to display (raster font format)? ftp://oak.oakland.edu/SimTel/msdos/ See also James Murray's graphics file formats FAQ: http://www.ora.com/centers/gff/gfffaq/index.htm  Subject 3.09: What is morphing/how is it done? Morphing is the name that has come to be applied to the technique ILM used in the movie "Willow", where one object changes into another by changing both its shape and picture detail. It was a 2D image manipulation, and has been done in different ways. The first method published was by Thad Beier at PDI. Michael Jackson famously used morphing in his music videos, notably "Black or White". The word is now used more generally. For more, see [Anderson], Chapter 3, page 5990, and Beier's http://www.hammerhead.com/thad/morph.html  Subject 3.10: How do I quickly draw a filled triangle? The easiest way is to render each line separately into an edge buffer. This buffer is a structure which looks like this (in C): struct { int xmin, xmax; } edgebuffer[YDIM]; There is one entry for each scan line on the screen, and each entry is to be interpreted as a horizontal line to be drawn from xmin to xmax. Since most people who ask this question are trying to write fast games on the PC, I'll tell you where to find code. Look at: ftp::/ftp.uwp.edu/pub/msdos/demos/programming/source ftp::/ftp.luth.se/pub/msdos/demos (Sweden) ftp::/NCTUCCCA.edu.tw:/PC/uwp/demos /www.wit.com:/mirrors/uwp/pub/msdos/demos">http://www.wit.com:/mirrors/uwp/pub/msdos/demos ftp::/ftp.cdrom.com:/demos See also Subject 3.03, which describes methods for filling polygons.  Subject 3.11: 3D Noise functions and turbulence in Solid texturing. See: ftp://gondwana.ecr.mu.oz.au/pub/ ftp://ftp.cis.ohiostate.edu/pub/siggraph92/siggraph92_C23.shar In it there are implementations of Perlin's noise and turbulence functions, (By the man himself) as well as Lewis' sparse convolution noise function (by D. Peachey) There is also some of other stuff in there (Musgrave's Earth texture functions, and some stuff on animating gases by Ebert). SPD (Standard Procedural Databases) package: ftp://avalon.chinalake.navy.mil/utils/SPD/ ftp://avalon.chinalake.navy.mil/utils/SPD/. Now moved to http://www.viewpoint.com/ References: [Ebert] Noise, Hypertexture, Antialiasing and Gesture, (Ken Perlin) in Chapter 6, (p.193), The disk accompanying the book is available from ftp://archive.cs.umbc.edu/pub/. For more info on this text/code see: http://www.cs.umbc.edu/~ebert/book/book.html For examples from a current course based on this book, see: http://www.seas.gwu.edu/graphics/ProcTexCourse/ Linke broken 21Jan03; will remove eventually if not fixed. [Watt:Animation] Threedimensional Nocie, Chapter 7.2.1 Simulating turbulance, Chapter 7.2.2  Subject 3.12: How do I generate realistic sythetic textures? For fractal mountains, trees and seashells: SPD (Standard Procedural Databases) package: ftp://avalon.chinalake.navy.mil/utils/SPD/ ftp://avalon.chinalake.navy.mil/utils/SPD/. Now moved to http://www.viewpoint.com/ ReactionDiffusion Algorithms: For an illustartion of the parameter space of a reaction diffusion system, check out the Xmorphia page at http://www.ccsf.caltech.edu/ismap/image.html References: [Ebert] Entire book devoted to this subject, with RenderMan(TM) and C code. [Watt:Animation] Procedural texture mapping and modelling, Chapter 7 "Generating Textures on Arbitrary Surfaces Using ReactionDiffusion" Greg Turk, Computer Graphics, Vol. 25, No. 4, pp. 289298 July 1991 (SIGGRAPH '91) http://www.cs.unc.edu:80/~turk/reaction_diffusion/reaction_diffusion.html A list of procedural texture synthesis related web pages http://www.threedgraphics.com/pixelloom/tex_synth.html  Subject 3.13: How do I convert between color models (RGB, HLS, CMYK, CIE etc)? References: [Watt:3D] pp. 313354 [Foley] pp. 563603  Subject 3.14: How is "GIF" pronounced? "GIF" is an acronymn for "Graphics Interchange Format." Despite the hard "G" in "Graphics," GIF is pronounced "JIF." Although we don't have a direct quote from the official CompuServe specification released June 1987, here is a quote from related CompuServe documentation, for CompuShow, a DOSbased image viewer used shortly thereafter: "The GIF (Graphics Interchange Format), pronounced "JIF", was designed by CompuServe ..." We also have a report that the principal author of the GIF spec, Steve Wilhite, says "it's pronounced JIF (like the peanut butter." See also http://www.60seconds.com/articles/86.html  Section 4. Curve Computations  Subject 4.01: How do I generate a Bezier curve that is parallel to another Bezier? You can't. The only case where this is possible is when the Bezier can be represented by a straight line. And then the parallel 'Bezier' can also be represented by a straight line. The situation is different for the broader class of rational Bezier curves. For example, these can represent circular arcs, and a parallel offset is just a concentric circular arc, also representable as a rational Bezier.  Subject 4.02: How do I split a Bezier at a specific value for t? A Bezier curve is a parametric polynomial function from the interval [0..1] to a space, usually 2D or 3D. Common Bezier curves use cubic polynomials, so have the form f(t) = a3 t^3 + a2 t^2 + a1 t + a0, where the coefficients are points in 3D. Blossoming converts this polynomial to a more helpful form. Let s = 1t, and think of trilinear interpolation: F([s0,t0],[s1,t1],[s2,t2]) = s0(s1(s2 c30 + t2 c21) + t1(s2 c21 + t2 c12)) + t0(s1(s2 c21 + t2 c12) + t1(s2 c12 + t2 c03)) = c30(s0 s1 s2) + c21(s0 s1 t2 + s0 t1 s2 + t0 s1 s2) + c12(s0 t1 t2 + t0 s1 t2 + t0 t1 s2) + c03(t0 t1 t2). The data points c30, c21, c12, and c03 have been used in such a way as to make the resulting function give the same value if any two arguments, say [s0,t0] and [s2,t2], are swapped. When [1t,t] is used for all three arguments, the result is the cubic Bezier curve with those data points controlling it: f(t) = F([1t,t],[1t,t],[1t,t]) = (1t)^3 c30 + 3(1t)^2 t c21 + 3(1t) t^2 c12 + t^3 c03. Notice that F([1,0],[1,0],[1,0]) = c30, F([1,0],[1,0],[0,1]) = c21, F([1,0],[0,1],[0,1]) = c12, _ F([0,1],[0,1],[0,1]) = c03. In other words, cij is obtained by giving F argument t's i of which are 0 and j of which are 1. Since F is a linear polynomial in each argument, we can find f(t) using a series of simple steps. Begin with f000 = c30, f001 = c21, f011 = c12, f111 = c03. Then compute in succession: f00t = s f000 + t f001, f01t = s f001 + t f011, f11t = s f011 + t f111, f0tt = s f00t + t f01t, f1tt = s f01t + t f11t, fttt = s f0tt + t f1tt. This is the de Casteljau algorithm for computing f(t) = fttt. It also has split the curve for the intervals [0..t] and [t..1]. The control points for the first interval are f000, f00t, f0tt, fttt, while those for the second interval are fttt, f1tt, f11t, f111. If you evaluate 3 F([1t,t],[1t,t],[1,1]) you will get the derivate of f at t. Similarly, 2*3 F([1t,t],[1,1],[1,1]) gives the second derivative of f at t, and finally using 1*2*3 F([1,1],[1,1],[1,1]) gives the third derivative. This procedure is easily generalized to different degrees, triangular patches, and Bspline curves.  Subject 4.03: How do I find a t value at a specific point on a Bezier? In general, you'll need to find t closest to your search point. There are a number of ways you can do this. Look at [Gems I, 607], there's a chapter on finding the nearest point on the Bezier curve. In my experience, digitizing the Bezier curve is an acceptable method. You can also try recursively subdividing the curve, see if you point is in the convex hull of the control points, and checking is the control points are close enough to a linear line segment and find the nearest point on the line segment, using linear interpolation and keeping track of the subdivision level, you'll be able to find t.  Subject 4.04: How do I fit a Bezier curve to a circle? Interestingly enough, Bezier curves can approximate a circle but not perfectly fit a circle. A common approximation is to use four beziers to model a circle, each with control points a distance d=r*4*(sqrt(2)1)/3 from the end points (where r is the circle radius), and in a direction tangent to the circle at the end points. This will ensure the midpoints of the Beziers are on the circle, and that the first derivative is continuous. The radial error in this approximation will be about 0.0273% of the circle's radius. Michael Goldapp, "Approximation of circular arcs by cubic polynomials" Computer Aided Geometric Design (#8 1991 pp.227238) Tor Dokken and Morten Daehlen, "Good Approximations of circles by curvaturecontinuous Bezier curves" Computer Aided Geometric Design (#7 1990 pp. 3341). See also http://www.whizkidtech.net/bezier/circle/ .  Section 5. 3D computations  Subject 5.01: How do I rotate a 3D point? Let's assume you want to rotate vectors around the origin of your coordinate system. (If you want to rotate around some other point, subtract its coordinates from the point you are rotating, do the rotation, and then add back what you subtracted.) In 3D, you need not only an angle, but also an axis. (In higher dimensions it gets much worse, very quickly.) Actually, you need 3 independent numbers, and these come in a variety of flavors. The flavor I recommend is unit quaternions: 4 numbers that square and add up to +1. You can write these as [(x,y,z),w], with 4 real numbers, or [v,w], with v, a 3D vector pointing along the axis. The concept of an axis is unique to 3D. It is a line through the origin containing all the points which do not move during the rotation. So we know if we are turning forwards or back, we use a vector pointing out along the line. Suppose you want to use unit vector u as your axis, and rotate by 2t degrees. (Yes, that's twice t.) Make a quaternion [u sin t, cos t]. You can use the quaternion  call it q  directly on a vector v with quaternion multiplication, q v q^1, or just convert the quaternion to a 3x3 matrix M. If the components of q are {(x,y,z),w], then you want the matrix M = {{12(yy+zz), 2(xywz), 2(xz+wy)}, { 2(xy+wz),12(xx+zz), 2(yzwx)}, { 2(xzwy), 2(yz+wx),12(xx+yy)}}. Rotations, translations, and much more are explained in all basic computer graphics texts. Quaternions are covered briefly in [Foley], and more extensively in several Graphics Gems, and the SIGGRAPH 85 proceedings. /* The following routine converts an angle and a unit axis vector * to a matrix, returning the corresponding unit quaternion at no * extra cost. It is written in such a way as to allow both fixed * point and floating point versions to be created by appropriate * definitions of FPOINT, ANGLE, VECTOR, QUAT, MATRIX, MUL, HALF, * TWICE, COS, SIN, ONE, and ZERO. * The following is an example of floating point definitions. #define FPOINT double #define ANGLE FPOINT #define VECTOR QUAT typedef struct {FPOINT x,y,z,w;} QUAT; enum Indices {X,Y,Z,W}; typedef FPOINT MATRIX[4][4]; #define MUL(a,b) ((a)*(b)) #define HALF(a) ((a)*0.5) #define TWICE(a) ((a)*2.0) #define COS cos #define SIN sin #define ONE 1.0 #define ZERO 0.0 */ QUAT MatrixFromAxisAngle(VECTOR axis, ANGLE theta, MATRIX m) { QUAT q; ANGLE halfTheta = HALF(theta); FPOINT cosHalfTheta = COS(halfTheta); FPOINT sinHalfTheta = SIN(halfTheta); FPOINT xs, ys, zs, wx, wy, wz, xx, xy, xz, yy, yz, zz; q.x = MUL(axis.x,sinHalfTheta); q.y = MUL(axis.y,sinHalfTheta); q.z = MUL(axis.z,sinHalfTheta); q.w = cosHalfTheta; xs = TWICE(q.x); ys = TWICE(q.y); zs = TWICE(q.z); wx = MUL(q.w,xs); wy = MUL(q.w,ys); wz = MUL(q.w,zs); xx = MUL(q.x,xs); xy = MUL(q.x,ys); xz = MUL(q.x,zs); yy = MUL(q.y,ys); yz = MUL(q.y,zs); zz = MUL(q.z,zs); m[X][X] = ONE  (yy + zz); m[X][Y] = xy  wz; m[X][Z] = xz + wy; m[Y][X] = xy + wz; m[Y][Y] = ONE  (xx + zz); m[Y][Z] = yz  wx; m[Z][X] = xz  wy; m[Z][Y] = yz + wx; m[Z][Z] = ONE  (xx + yy); /* Fill in remainder of 4x4 homogeneous transform matrix. */ m[W][X] = m[W][Y] = m[W][Z] = m[X][W] = m[Y][W] = m[Z][W] = ZERO; m[W][W] = ONE; return (q); } /* The routine just given, MatrixFromAxisAngle, performs rotation about * an axis passing through the origin, so only a unit vector was needed * in addition to the angle. To rotate about an axis not containing the * origin, a point on the axis is also needed, as in the following. For * mathematical purity, the type POINT is used, but may be defined as: #define POINT VECTOR */ QUAT MatrixFromAnyAxisAngle(POINT o, VECTOR axis, ANGLE theta, MATRIX m) { QUAT q; q = MatrixFromAxisAngle(axis,theta,m); m[X][W] = o.x(MUL(m[X][X],o.x)+MUL(m[X][Y],o.y)+MUL(m[X][Z],o.z)); m[Y][W] = o.y(MUL(m[Y][X],o.x)+MUL(m[Y][Y],o.y)+MUL(m[Y][Z],o.z)); m[Z][W] = o.x(MUL(m[Z][X],o.x)+MUL(m[Z][Y],o.y)+MUL(m[Z][Z],o.z)); return (q); } /* An axis can also be specified by a pair of points, with the direction * along the line assumed from the ordering of the points. Although both * the previous routines assumed the axis vector was unit length without * checking, this routine must cope with a more delicate possibility. If * the two points are identical, or even nearly so, the axis is unknown. * For now the auxiliary routine which makes a unit vector ignores that. * It needs definitions like the following for floating point. #define SQRT sqrt #define RECIPROCAL(a) (1.0/(a)) */ VECTOR Normalize(VECTOR v) { VECTOR u; FPOINT norm = MUL(v.x,v.x)+MUL(v.y,v.y)+MUL(v.z,v.z); /* Better to test for (near)zero norm before taking reciprocal. */ FPOINT scl = RECIPROCAL(SQRT(norm)); u.x = MUL(v.x,scl); u.y = MUL(v.y,scl); u.z = MUL(v.z,scl); return (u); } QUAT MatrixFromPointsAngle(POINT o, POINT p, ANGLE theta, MATRIX m) { QUAT q; VECTOR diff, axis; diff.x = p.xo.x; diff.y = p.yo.y; diff.z = p.zo.z; axis = Normalize(diff); return (MatrixFromAnyAxisAngle(o,axis,theta,m)); }  Subject 5.02: What is ARCBALL and where is the source? Arcball is a general purpose 3D rotation controller described by Ken Shoemake in the Graphics Interface '92 Proceedings. It features good behavior, easy implementation, cheap execution, and optional axis constraints. A Macintosh demo and electronic version of the original paper (Microsoft Word format) may be obtained from ftp::/ftp.cis.upenn.edu/pub/graphics. Complete source code appears in Graphics Gems IV pp. 175192. A fairly complete sketch of the code appeared in the original article, in Graphics Interface 92 Proceedings, available from Morgan Kaufmann. The original arcball code was written for IRIS GL. A translation into OpenGL/GLUT, and for IRIS Performer, may be found at: http://cs.anu.edu.au/people/Hugh.Fisher/3dstuff/  Subject 5.03: How do I clip a polygon against a rectangle? This is the SutherlandHodgman algorithm, an old favorite that should be covered in any textbook. See the selected list below. According to Vatti (q.v.) "This [algorithm] produces degenerate edges in certain concave / self intersecting polygons that need to be removed as a special extension to the main algorithm" (though this is not a problem if all you are doing with the results is scan converting them.) It should be noted that the SutherlandHodgman algorithm may be used to clip a polygon against any convex polygon. Cf. also Subject 5.04. [Foley, van Dam]: Section 3.14.1 (pp 124  126) [Hearn]: Section 68, pp 237  242 (with actual C code!) See also http://www.csclub.uwaterloo.ca/u/mpslager/articles/sutherland/wr.html  Subject 5.04: How do I clip a polygon against another polygon? Klamer Schutte, klamer@ph.tn.tudelft.nl has developed and implemented some code in C++ to perform clipping of two possibly concave 2D polygons. A description can be found at: /www.ph.tn.tudelft.nl:/People/klamer/clippoly_entry.html">http://www.ph.tn.tudelft.nl:/People/klamer/clippoly_entry.html To compile the source code you will need a C++ compiler with templates, such as g++. The source code is available at: ftp://ftp.ph.tn.tudelft.nl/pub/klamer/clippoly.tar.gz  See also http://home.attbi.com/~msleonov/pbcomp.html, which extends the above to permit holes. Alan Murta released a polygon clipper library (in C) which uses a modified version of the Vatti algorithm: http://www.cs.man.ac.uk/aig/staff/alan/software/index.html References: Weiler, K. "Polygon Comparison Using a Graph Representation", SIGGRAPH 80 pg. 1018 Vatti, Bala R. "A Generic Solution to Polygon Clipping", Communications of the ACM, July 1992, Vol 35, No. 7, pg. 5763  Subject 5.05: How do I find the intersection of a line and a plane? If the plane is defined as: a*x + b*y + c*z + d = 0 and the line is defined as: x = x1 + (x2  x1)*t = x1 + i*t y = y1 + (y2  y1)*t = y1 + j*t z = z1 + (z2  z1)*t = z1 + k*t Then just substitute these into the plane equation. You end up with: t =  (a*x1 + b*y1 + c*z1 + d)/(a*i + b*j + c*k) When the denominator is zero, the line is contained in the plane if the numerator is also zero (the point at t=0 satisfies the plane equation), otherwise the line is parallel to the plane.  Subject 5.06: How do I determine the intersection between a ray and a triangle? First find the intersection between the ray and the plane in which the triangle is situated. Then see if the point of intersection is inside the triangle. Details may be found in [O'Rourke (C)] pp.226238, whose code is at http://cs.smith.edu/~orourke/ . Efficient code complete with statistical tests is described in the Mo:ller Trumbore paper in J. Graphics Tools (C code downloadable from there): http://www.acm.org/jgt/papers/MollerTrumbore97/ See also the full paper: http://www.Graphics.Cornell.EDU/pubs/1997/MT97.html See also the "3D Object Intersection" page, described in Subject 0.05.  Subject 5.07: How do I determine the intersection between a ray and a sphere Given a ray defined as: x = x1 + (x2  x1)*t = x1 + i*t y = y1 + (y2  y1)*t = y1 + j*t z = z1 + (z2  z1)*t = z1 + k*t and a sphere defined as: (x  l)**2 + (y  m)**2 + (z  n)**2 = r**2 Substituting in gives the quadratic equation: a*t**2 + b*t + c = 0 where: a = i**2 + j**2 + k**2 b = 2*i*(x1  l) + 2*j*(y1  m) + 2*k*(z1  n) c = (x1l)**2 + (y1m)**2 + (z1n)**2  r**2; If the discriminant of this equation is less than 0, the line does not intersect the sphere. If it is zero, the line is tangential to the sphere and if it is greater than zero it intersects at two points. Solving the equation and substituting the values of t into the ray equation will give you the points. Reference: [Glassner:RayTracing] See also the "3D Object Intersection" page, described in Subject 0.05.  Subject 5.08: How do I find the intersection of a ray and a Bezier surface? Joy I.K. and Bhetanabhotla M.N., "Ray tracing parametric surfaces utilizing numeric techniques and ray coherence", Computer Graphics, 16, 1986, 279286 Joy and Bhetanabhotla is only one approach of three major method classes: tessellation, direct computation, and a hybrid of the two. Tessellation is relatively easy (you intersect the polygons making up the tessellation) and has no numerical problems, but can be inaccurate; direct is cheap on memory, but very expensive computationally, and can (and usually does) suffer from precision problems within the root solver; hybrids try to blend the two. Reference: [Glassner:RayTracing] See also the "3D Object Intersection" page, described in Subject 0.05.  Subject 5.09: How do I ray trace caustics? See the work of Henrik Wann Jensen at http://graphics.ucsd.edu/~henrik/ @inproceedings{jrcnls96 , author = "Henrik Wann Jensen" , title = "Rendering Caustics on NonLambertian Surfaces" , booktitle = "Proc. Graphics Interface '96" , pages = "116121" , location = "Toronto" , year = 1996 } Metropolis Light Transport handles this phenomenon well: http://wwwgraphics.stanford.edu/papers/metro/ Bidirectional path tracing also handles caustics. http://graphics.stanford.EDU/papers/veach_thesis/ (Chapter 10) http://www.graphics.cornell.edu/~eric/thesis/ Some older references: An expensive answer: @InProceedings{mitchell1992illumination, author = "Don P. Mitchell and Pat Hanrahan", title = "Illumination From Curved Reflectors", year = "1992", month = "July", volume = "26", booktitle = "Computer Graphics (SIGGRAPH '92 Proceedings)", pages = "283291", keywords = "caustics, interval arithmetic, ray tracing", editor = "Edwin E. Catmull", } A cheat: @Article{inakage1986caustics, author = "Masa Inakage", title = "Caustics and Specular Reflection Models for Spherical Objects and Lenses ", pages = "379383", journal = "The Visual Computer", volume = "2", number = "6", year = "1986", keywords = "ray tracing effects", } Very specialized: @Article{yuan1988gemstone, author = "Ying Yuan and Tosiyasu L. Kunii and Naota Inamato and Lining Sun ", title = "Gemstone Fire: Adaptive Dispersive Ray Tracing of Polyhedrons", year = "1988", month = "November", journal = "The Visual Computer", volume = "4", number = "5", pages = "25970", keywords = "caustics", }  Subject 5.10: What is the marching cubes algorithm? The marching cubes algorithm is used in volume rendering to construct an isosurface from a 3D field of values. The 2D analog would be to take an image, and for each pixel, set it to black if the value is below some threshold, and set it to white if it's above the threshold. Then smooth the jagged black outlines by skinning them with lines. The marching cubes algorithm tests the corner of each cube (or voxel) in the scalar field as being either above or below a given threshold. This yields a collection of boxes with classified corners. Since there are eight corners with one of two states, there are 256 different possible combinations for each cube. Then, for each cube, you replace the cube with a surface that meets the classification of the cube. For example, the following are some 2D examples showing the cubes and their associated surface.   +      +   + :::'  ::::::: ::::   '::: :'  ::::::: ::::  . ':     ::::  ::.  +  + +  +   + +   The result of the marching cubes algorithm is a smooth surface that approximates the isosurface that is constant along a given threshold. This is useful for displaying a volume of oil in a geological volume, for example. References: "Marching Cubes: A High Resolution 3D Surface Construction Algorithm", William E. Lorensen and Harvey E. Cline, Computer Graphics (Proceedings of SIGGRAPH '87), Vol. 21, No. 4, pp. 163169. [Watt:Animation] pp. 302305 and 313321 [Schroeder] For alternatives to the (patented; cf. Subj. 5.11) marching cubes algorithm, see http://www.unchainedgeometry.com/jbloom/papers/index.html under "Implicit Surface Polygonization."  Subject 5.11: What is the status of the patent on the "marching cubes" algorithm? United States Patent Number: 4,710,876 Date of Patent: Dec. 1, 1987 Inventors: Harvey E. Cline, William E. Lorensen Assignee: General Electric Company Title: "System and Method for the Display of Surface Structures Contained Within the Interior Region of a Solid Body" Filed: Jun. 5, 1985 http://www.delphion.com/ Type in "4710876" (w/o commas, w/o quotes) into their search engine. United States Patent Number: 4,885,688 Date of Patent: Dec. 5, 1989 Inventor: Carl R. Crawford Assignee: General Electric Company Title: "Minimization of Directed Points Generated in ThreeDimensional Dividing Cubes Method" Filed: Nov. 25, 1987 Access as above. For alternative, unpatented algorithms, cf. Subj. 5.10.  Subject 5.12: How do I do a hidden surface test (backface culling) with 3D points? Just define all points of all polygons in clockwise order. c = (x3*((z1*y2)(y1*z2))+ (y3*((x1*z2)(z1*x2))+ (z3*((y1*x2)(x1*y2))+ x1,y1,z1, x2,y2,z2, x3,y3,z3 = the first three points of the polygon. If c is positive the polygon is visible. If c is negative the polygon is invisible (or the other way).  Subject 5.13: Where can I find algorithms for 3D collision detection? Check out "proxima", from Purdue, which is a C++ library for 3D collision detection for arbitrary polyhedra. It's a nice system; the algorithms are sophisticated, but the code is of modest size. ftp://ftp.cs.purdue.edu/pub/pse/PROX/ For information about the I_COLLIDE 3D collision detection system http://www.cs.unc.edu/~geom/I_COLLIDE.html "Fast Collision Detection of Moving Convex Polyhedra", Rich Rabbitz, Graphics Gems IV, pages 83109, includes source in C. SOLID: "a library for collision detection of threedimensional objects undergoing rigid motion and deformation. SOLID is designed to be used in interactive 3D graphics applications, and is especially suited for collision detection of objects and worlds described in VRML. Written in standard C++, compiles under GNU g++ version 2.8.1 and Visual C++ 5.0." See: http://www.win.tue.nl/cs/tt/gino/solid/ SWIFT++: a C++ library for collision detection, exact and approximate distance computation, and contact determination of threedimensional polyhedral objects undergoing rigid motion. Some preliminary results indicate that it is faster than ICOLLIDE and VCLIP, and more robust than ICOLLIDE. http://www.cs.unc.edu/~geom/SWIFT++ ColDet: C++ library for 3D collison detection. Works on generic polyhedra, and even polygon soups. Uses bounding box hierarchies and triangle intersection tests. Released as open source under LGPL. Tested on Windows, MacOS, and Linux. http://photoneffect.com/coldet/ . Terdiman's lib, which might need less RAM than the above: http://www.codercorner.com/Opcode.htm  Subject 5.14: How do I perform basic viewing in 3D? We describe the shape and position of objects using numbers, usually (x,y,z) coordinates. For example, the corners of a cube might be {(0,0,0), (1,0,0), (1,1,0), (0,1,0), (0,0,1), (1,0,1), (1,1,1), (0,1,1)}. A deep understanding of what we are saying with these numbers requires mathematical study, but I will try to keep this simple. At the least, we must understand that we have designated some point in space as the origincoordinates (0,0,0)and marked off lines in 3 mutually perpendicular directions using equally spaced units to give us (x,y,z) values. It might be helpful to know if we are using 1 to mean 1 foot, 1 meter, or 1 parsec; the numbers alone do not tell us. A picture on a screen is two steps removed from the 3D world it depicts. First, it is a 2D projection; and second, it is a finite resolution approximation to the infinitely precise projection. I will ignore the approximation (sampling) for now. To know what the projection looks like, we need to know where our viewpoint is, and where the plane of the projection is, both in the 3D world. Think of it as looking out a window into a scene. As artists discovered some 500 years ago, each point in the world appears to be at a point on the window. If you move your head or look out a different window, everything changes. When the mathematicians understood what the artists were doing, they invented perspective geometry. If your viewpoint is at the origin(0,0,0)and the window sits parallel to the xy plane but at z=1, projection is no more than (x,y,z) in the world appears at (x/z,y/z,1) on the plane. Distant objects will have large z values, causing them to shrink in the picture. That's perspective. The trick is to take an arbitrary viewpoint and plane, and transform the world so we have the simple viewing situation. There are two steps: move the viewpoint to the origin, then move the viewplane to the z=1 plane. If the viewpoint is at (vx,vy,vz), transform every point by the translation (x,y,z) > (xvx,yvy,zvz). This includes the viewpoint and the viewplane. Now we need to rotate so that the z axis points straight at the viewplane, then scale so it is 1 unit away. After all this, we may find ourselves looking out upside down. It is traditional to specify some direction in the world or viewplane as "up", and rotate so the positive y axis points that way (as nearly as possible if it's a world vector). Finally, we have acted so far as if the window was the entire plane instead of a limited portal. A final shift and scale transforms coordinates in the plane to coordinates on the screen, so that a rectangular region of interest (our "window") in the plane fills a rectangular region of the screen (our "canvas" if you like). Details of how to define and perform the rotation of the viewplane have been left out, but see "How can I aim a camera in a specific direction?" elsewhere in this FAQ. One simple way to designate a plane is with the point closest to the origin, call it D. Then a point P is on the plane if D.P = D.D; or using d = D and N = D/d, if N.P = d. Aim the camera with N, and scale with d. A further practical difficulty is the need to clip away parts of the world behind us, so (x,y,z) doesn't pop up at (x/z,y/z,1). (Notice the mathematics of projection alone would allow that!) In fact ordinarily a clipping box, the "viewing frustum", is used to eliminate parts of the scene outside the window left or right, top or bottom, and too close or too far. All the viewing transformations can be done using translation, rotation, scale, and a final perspective divide. If a 4x4 homogeneous matrix is used, it can represent everything needed, which saves a lot of work.  Subject 5.15: How do I optimize/simplify a 3D polygon mesh? References: "Mesh Optimization" Hoppe, DeRose Duchamp, McDonald, Stuetzle, ACM COMPUTER GRAPHICS Proceedings, Annual Conference Series, 1993. "ReTiling Polygonal Surfaces", Greg Turk, ACM Computer Graphics, 26, 2, July 1992 "Decimation of Triangle Meshes", Schroeder, Zarge, Lorensen, ACM Computer Graphics, 26, 2 July 1992 "Simplification of Objects Rendered by Polygonal Approximations", Michael J. DeHaemer, Jr. and Michael J. Zyda, Computer & Graphics, Vol. 15, No. 2, 1991, Great Britain: Pergamon Press, pp. 175184. "Topological Refinement Procedures for Triangular Finite Element Procedures", S. A. Cannan, S. N. Muthukrishnan and R. P. Phillips, Engineering With Computers, No. 12, p. 243255, 1996. "Progressive Meshes", Hoppe, SIGGRAPH 96, http://research.microsoft.com/~hoppe/siggraph96pm/paper.htm Several papers by Michael Garland (quadricbased error metric): http://graphics.cs.uiuc.edu/~garland/ Demos: By Stan Melax: http://www.cs.ualberta.ca/~melax/polychop/ By Stefan Krause: http://www.stefankrause.com [Gnu Open Source] By "klaudius": http://www.klaudius.free.fr  Subject 5.16: How can I perform volume rendering? Two principal methods can be used:  Ray casting or fronttoback, where the volume is behind the projection plane. A ray is projected from each point in the projection plane through the volume. The ray accumulates the properties of each voxel it passes through.  Object order or backtofront, where the projection plane is behind the volume. Each slice of the volume is projected on the projection plane, from the farest plane to the nearest plane. You can also use the marchingcubes algorithm, if the extraction of surfaces from the data set is easy to do (see Subject 5.10). Here is one algorithm to do fronttoback volume rendering: Set up a projection plane as a plane tangent to a sphere that encloses the volume. From each pixel of the projection plane, cast a ray through the volume by using a Bresenham 3D algorithm. The ray accumulates properties from each voxel intersected, stopping when the ray exits the volume. The pixel value on the projection plane determines the final color of the ray. For unshaded images (i.e., without gradient and light computations), if C is the ray color t the ray transparency C' the new ray color t' the new ray transparency Cv the voxel color tv the voxel transparency then: C' = C + t*Cv and t' = t * tv with initial values: C = 0.0 and t = 1.0 An alternate version: instead of C' = C + t*Cv , use : C' = C + t*Cv*(1tv)^p with p a float variable. Sometimes this gives the best results. When the ray has accumulated transparency, if it becomes negligible (e.g., t<0.1), the process can stop and proceed to the next ray. References: Bresenham 3D:  http://www.sct.gu.edu.au/~anthony/info/graphics/bresenham.procs  [Gems IV] p. 366 Volume rendering:  [Watt:Animation] pp. 297321  IEEE Computer Graphics and application Vol. 10, Nb. 2, March 1990  pp. 2453  "Volume Visualization" Arie Kaufman  Ed. IEEE Computer Society Press Tutorial  "Efficient Ray Tracing of Volume Data" Marc Levoy  ACM Transactions on Graphics, Vol. 9, Nb 3, July 1990  Subject 5.17: Where can I get the spline description of the famous teapot etc.? See the Standard Procedural Databases software, whose official distribution site is http://www.acm.org/tog/resources/SPD/ This database contains much useful 3D code, including spline surface tessellation, for the teapot.  Subject 5.18: How can the distance between two lines in space be computed? Let x_i be points on the respective lines and n_i unit direction vectors along the lines. Then the distance is  (x_1  x_0)^T (n_1 X n_0)  /  n_1 X n_0 . Often one wants the points of closest approach as well as the distance. The following is robust C code from Seth Teller that computes the these points on two 3D lines. It also classifies the lines as parallel, intersecting, or (the generic case) skew. What's listed below shows the main ideas; the full code is at http://graphics.lcs.mit.edu/~seth/geomlib/linelinecp.c // computes pB ON line B closest to line A // computes pA ON line A closest to line B // return: 0 if parallel; 1 if coincident; 2 if generic (i.e., skew) int line_line_closest_points3d ( POINT *pA, POINT *pB, // computed points const POINT *a, const VECTOR *adir, // line A, pointnormal form const POINT *b, const VECTOR *bdir ) // line B, pointnormal form { static VECTOR Cdir, *cdir = &Cdir; static PLANE Ac, *ac = &Ac, Bc, *bc = &Bc; // connecting line is perpendicular to both vcross ( cdir, adir, bdir ); // check for nearparallel lines if ( !vnorm( cdir ) ) { *pA = *a; // all points are closest *pB = *b; return 0; // degenerate: lines parallel } // form plane containing line A, parallel to cdir plane_from_two_vectors_and_point ( ac, cdir, adir, a ); // form plane containing line B, parallel to cdir plane_from_two_vectors_and_point ( bc, cdir, bdir, b ); // closest point on A is line A ^ bc intersect_line_plane ( pA, a, adir, bc ); // closest point on B is line B ^ ac intersect_line_plane ( pB, b, bdir, ac ); // distinguish intersecting, skew lines if ( edist( pA, pB ) < 1.0E5F ) return 1; // coincident: lines intersect else return 2; // distinct: lines skew } Also Dave Eberly has code for computing distance between various geometric primitives, including MinLineLine(), at http://www.magicsoftware.com  Subject 5.19: How can I compute the volume of a polyhedron? Assume that the surface is closed, every face is a triangle, and the vertices of each triangle are oriented ccw from the outside. Let Volume(t,p) be the signed volume of the tetrahedron formed by a point p and a triangle t. This may be computed by a 4x4 determinant, as in [O'Rourke (C), p.26]. Choose an arbitrary point p (e.g., the origin), and compute the sum Volume(t_i,p) for every triangle t_i of the surface. That is the volume of the object. The justification for this claim is nontrivial, but is essentially the same as the justification for the computation of the area of a polygon (Subject 2.01). C Code available at http://cs.smith.edu/~orourke/ and http://www.acm.org/jgt/papers/Mirtich96/ . For computing the volumes of nd convex polytopes, there is a C implementation by Bueeler and Enge of various algorithms available at http://www.Mathpool.UniAugsburg.DE/~enge/Volumen.html .  Subject 5.20: How can I decompose a polyhedron into convex pieces? Usually this problem is interpreted as seeking a collection of pairwise disjoint convex polyhedra whose set union is the original polyhedron P. The following facts are known about this difficult problem: o Not every polyhedron may be partitioned by diagonals into tetrahedra. A counterexample is due to Scho:nhardt [O'Rourke (A), p.254]. o Determining whether a polyhedron may be so partitioned is NPhard, a result due to Seidel & Ruppert [Disc. Comput. Geom. 7(3) 227254 (1992).] o Removing the restriction to diagonals, i.e., permitting socalled Steiner points, there are polyhedra of n vertices that require n^2 convex pieces in any decomposition. This was established by Chazelle [SIAM J. Comput. 13: 488507 (1984)]. See also [O'Rourke (A), p.256] o An algorithm of Palios & Chazelle guarantees at most O(n+r^2) pieces, where r is the number of reflex edges (i.e., grooves). [Disc. Comput. Geom. 5:505526 (1990).] o Barry Joe's geompack package, at ftp://ftp.cs.ualberta.ca/pub/geompack, includes a 3D convex decomposition Fortran program. o There seems to be no other publicly available code.  Subject 5.21: How can the circumsphere of a tetrahedron be computed? Let a, b, c, and d be the corners of the tetrahedron, with ax, ay, and az the components of a, and likewise for b, c, and d. Let a denote the Euclidean norm of a, and let a x b denote the cross product of a and b. Then the center m and radius r of the circumsphere are given by    da^2 [(ba)x(ca)] + ca^2 [(da)x(ba)] + ba^2 [(ca)x(da)]    r=   bxax byay bzaz  2  cxax cyay czaz   dxax dyay dzaz  and da^2 [(ba)x(ca)] + ca^2 [(da)x(ba)] + ba^2 [(ca)x(da)] m= a +   bxax byay bzaz  2  cxax cyay czaz   dxax dyay dzaz  Some notes on stability:  Note that the expression for r is purely a function of differences between coordinates. The advantage is that the relative error incurred in the computation of r is also a function of the _differences_ between the vertices, and is not influenced by the _absolute_ coordinates of the vertices. In most applications, vertices are usually nearer to each other than to the origin, so this property is advantageous. Similarly, the formula for m incurs roundoff error proportional to the differences between vertices, but not proportional to the absolute coordinates of the vertices until the final addition.  These expressions are unstable in only one case: if the denominator is close to zero. This instability, which arises if the tetrahedron is nearly degenerate, is unavoidable. Depending on your application, you may want to use exact arithmetic to compute the value of the determinant. See http://www.geom.umn.edu/software/cglist/alg.html or http://www.cs.cmu.edu/~quake/robust.html  Subject 5.22: How do I determine if two triangles in 3D intersect? Let the two triangles be T1 and T2. If T1 lies strictly to one side of the plane containing T2, or T2 lies strictly to one side of the plane containing T1, the triangles do not intersect. Otherwise, compute the line of intersection L between the planes. Let Ik be the interval (Tk inter L), k=1,2. Either interval may be empty. T1 and T2 intersect iff I1 and I2 overlap. This method is decribed in Tomas Mo:ller, "A fast triangletriangle intersection test," J. Graphics Tools 2(2) 2530 1997. C code at http://www.acm.org/jgt/papers/Moller97/ . See also http://www.ce.chalmers.se/staff/tomasm/code/ http://www.magicsoftware.com/MgcIntersection.html See also the "3D Object Intersection" page, described in Subject 0.05. NASA's "Intersect" code will intersect any number of triangulated surfaces provided that each of the surfaces is both closed and manifold. http://www.nas.nasa.gov/~aftosmis/cart3d/surfaceModeling.html#AuxProgs Based on "Robust and Efficient Cartesian Mesh Generation for ComponentBased Geometry" AIAA Paper 970196. Michael Aftosmis.  Subject 5.23: How can a 3D surface be reconstructed from a collection of points? This is a difficult problem. There are two main variants: (1) when the points are organized into parallel slices through the object; (2) when the points are unorganized. For (1), see this survey: D. Meyers, S. Skinner, K. Sloan. "Surfaces from Contours" ACM Trans. Graph. 11(3) Jul 1992, 228258. http://www.acm.org/pubs/citations/journals/tog/1992113/p228meyers/ Code (NUAGES) is available at http://wwwsop.inria.fr/prisme/logiciel/nuages.html.en ftp://ftpsop.inria.fr/prisme/NUAGES/Nuages/NUAGES_SRC.tar.gz For (2), see this paper: H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, W. Stuetzle "Surface reconstruction from unorganized points" Proc. SIGGRAPH '92, 7178. and P. Kumar's collection of links at http://members.tripod.com/~GeomWiz/www.sites.html New code, Cocone, written with CGAL, based on recent work by N. Amenta, S. Choi, T. K. Dey and N. Leekha: http://www.cis.ohiostate.edu/~tamaldey/cocone.html  Subject 5.24: How can I find the smallest sphere enclosing a set of points in 3D? Although not obvious, the smallest sphere enclosing a set of points in any dimension can be found by Linear Programming. This was proved by Emo Welzl in the paper, "Smallest enclosing disks (balls and ellipsoids)" [Lecture Notes Comput. Sci., 555, SpringerVerlag, 1991, 359370]. + Code developed by Bernd Gaertner available (GNU General Public License) at: http://www.inf.ethz.ch/~gaertner/miniball.html This code is remarkably efficient: e.g., 2 seconds for 10^6 points in 3D on an UltraSparc II. See also Dave Eberly's direct implementation of Welzl's algorithm: http://www.magicsoftware.com/MgcContainment3D.html  Subject 5.25: What's the big deal with quaternions? This could mean "Why do they evoke such heated debate?" or "What are their virtues?" The heat of debate is hard to explain, but it's been happening for many decades. When Gibbs first deprecated the quaternion product and split it into a cross product and a dot product, one outraged Victorian called the result a "hermaphrodite monster"  and that before the Internet's flame wars. Generally, the quaternion advocates seem to feel the opponents are lazy or thickheaded, and that deeper understanding of quaternions would lead to deeper appreciation. The opponents don't appreciate that attitude, and seem to feel the advocates are snooty or sheep, and that matrices and such are less abstract and do just fine. (Advocates of Clifford algebra would claim that both sides are mired in the past.) Passion aside, quaternions have appropriate uses, as do their alternatives. Someone new to the debate first needs to know what quaternions are, and what they're supposed to be good for. Quaternions are a quadruple of numbers, used to represent 3D rotations. q = [x,y,z,w] = [(x,y,z),w] = [V,w] The "norm" of a quaternion N(q) is conventionally the sum of the squares of the four components. Some writers confuse this with the magnitude, which is the square root of the norm. Another common misconception is that only quaternions of unit norm can be used, those with the sum of the four squares equal to 1, but that is wrong (though they are preferred). [U sin a,cos a] rotates by angle 2a around unit vector U Popular nonquaternion options are 3x3 special orthogonal matrices (9 numbers with constraints), Euler angles (3 numbers), axisangle (4 numbers), and angular velocity vectors (3 numbers). None of these options actually _are_ rotations, which are physical; they _represent_ rotations. The distinction is important because, for example, it is common to use an axisangle with an angle greater than 360 degrees to tell an animation system to spin an object more than a full turn, something a matrix cannot say. In mathematics, the usual meaning of a rotation would not allow the multiple spin version, which can lead to confusing debates. q and q represent the same rotation Two rotations, the physical things, can be applied one after the other. Assuming the two rotation axes have a least one point in common, the result will be another rotation. Some rotation representations handle this gracefully, some don't. For quaternions and matrices, forms of multiplication are defined such that the product gives the desired result. For Euler angles especially there is no simple computation. q = q2 q1 = [V2,w2][V1,w1] = [V2xV1+w2V1+w1V2,w2w1V2.V1] Every rotation has a reverse rotation, such that the combination of the two leaves an object as it was. (Rotations are an algebraic "group".) Euler angles make reversals difficult to compute. Other representations, including quaternions, make them simple. reverse([V,w]) = [V,w] q^(1) = [V,w]/(V.V+ww) Two physical rotations are also more or less similar. Unit quaternions do a particularly good job of representing similar rotations with similar numbers. similar(q1,q2) = q1.q2 =  x1x2+y1y2+z1z2+w1w2  Points in space, the physical things, are normally represented as 3 or 4 numbers. The effect of a rotation on a collection of points can be computed from the representation of the rotation, and here matrices seem fastest, using three dot products. Using their own product twice, quaternions are a bit less efficient. (They are usually converted to matrices at the last minute.) p2 = q p1 q^(1) Sequences of rotations can be interpolated, so that the object being turned is rotated to specific poses at specific times. This motivated Ken Shoemake's early use of quaternions in computer graphics, as published in 1985. He used an analog of linear interpolation (sometimes called "lerp") that he called "Slerp", and also introduced an analog of a piecewise Bezier curve. A few years later in some course notes he described another curve variation he called "Squad", which still seems to be popular. Later authors have proposed many alternatives. sin (1t)A sin tA Slerp(q1,q2;t) = q1  + q2 , cos A = q1.q2 sin A sin A Squad(q1,a1,b2,q2;t) = Slerp(Slerp(q1,q2;t), Slerp(a1,b2;t); 2t(1t)) Physics simulation, aerospace control, and robotics are examples of computations which also depend on rotation representation. Constrained rotations like a wheel on an axle or the elbow bend of a robot typically use specialized representations, such as an angle alone. In many general situations, however, quaternions have proved valuable. 2 dq = W q dt, W is the angular velocity vector User interfaces for 3D rotation also require a representation. Direct manipulation interfaces typically use angles for jointed figures, but for freer manipulation may use quaternions, as in Arcball or throughthelens camera control. As Shoemake's _full_ Graphics Gems code for Arcball demonstrates (with the [CAPS LOCK] key), any rotation can be graphed as an arc on a sphere. (Not to be confused with the quaternion unit sphere in 4D.) Whether quaternions, or any other representation, are helpful for numeric presentation and input seems a matter of taste and circumstance. q = U2 U1^(1) = [U1xU2,U1.U2] Because of their metric properties for representing rotations, unit quaternions are most common. Advocates frequently point out that it is far cheaper to normalize the length of a nonzero quaternion than to bring a matrix back to rotation form. Also Shoemake's later conversion code cheaply creates a correct rotation matrix from _any_ quaternion (found with his Euler angle code from Graphics Gems, which does the same for all 24 variations of that representation). Normalize(q) = q/Sqrt(q.q) Comparisons to Euler angles may mention "gimbal lock" (frequently misspelled) as a disadvantage quaternions avoid. In the physical world where gyroscopes are mounted on nested pivots, which are called gimbals, locking is a real problem quaternions cannot help. What's usually meant is that because the similarity of rotations organizes them somewhat like a sphere, while similarity of vectors is quite different, an inevitable misfit plagues Euler angles. This can show up in behavior much like physical gimbal lock, but also in other ways. The difficulties are topological, and aiming runs into them as well, even if quaternions are used. Quaternion authors who propose using curves in the vector space of quaternion logarithms often risk the misfit unawares. Frankly, you must pick through the literature carefully, whether informal and online or refereed and printed, because mistakes are tragically common. To explore Graphics Gems code, see "Where is all the source?" in this FAQ. To read more about quaternions, you have many options. Since they were discovered in 1843 by Hamilton (the same Irish mathematician and physicist whose name shows up in quantum mechanics), quaternions have found many friends, as a web search will reveal. Quaternions can be approached and applied in numerous different ways, so if you keep looking it's likely you will find something that suits your taste and your needs. (Subject 0.04) [Eberly], Ch. 2. http://www.maths.tcd.ie/pub/HistMath/People/Hamilton/OnQuat/ Hamilton's original paper. Not easy for today's readers. K. Shoemake. Animating Rotation with Quaternion Curves. Proceedings of Siggraph 85. Original animation method. Covers all the basics. ftp://ftp.cis.upenn.edu/pub/graphics/shoemake/ Later Shoemake tutorial. More complete than most authors. Graphics Gems IV, various authors, various articles. As usual, a helpful source of code and discussion. ftp://ftp.netcom.com/pub/hb/hbaker/quaternion/ Henry Baker collects good quaternion stuff. Access spotty. http://linux.rice.edu/~rahul/hbaker/quaternion/ Henry Baker collection with more reliable access. http://www.eecs.wsu.edu/~hart/papers/vqr.pdf Visualizing quaternion rotation. May help build intuition. http://graphics.stanford.edu/courses/cs348c95fall /software/quatdemo/ The GL code implementing above Hart et al. paper. http://www.diku.dk/students/myth/quat.html Mathematical, but not too fast and not too fancy. http://www.cs.berkeley.edu/~laura/cs184/quat/quaternion.html Laura Downs covers the fundamentals. http://graphics.cs.ucdavis.edu/GraphicsNotes/Quaternions /Quaternions.htm Ken Joy covers the fundamentals. http://www.gg.caltech.edu/STC/rr_sig97.html Hightech interpolation method. Demanding but rewarding. Duff, Tom: Quaternion Splines for Animating Orientation, Proceedings of the USENIX Association Second Computer Graphics Workshop (held in Monterey, CA 1213 Dec. 1985), pp. 5462. Subdivision paper in odd place. Author last seen at Pixar.  Subject 5.26: How can I aim a camera in a specific direction? What's needed is a method for creating a rotation that turns one unit vector to line up with another. To aim at an object, you can subtract the position of the camera from the position of the object to get a vector which you then normalize. The vector you want to turn is the camera forward vector, commonly a unit vector along the camera z axis. Be warned that more than one rotation can achieve aim alone. (The issue is rather complicated, as laid out in Ken Shoemake's article on twist reduction in Graphics Gems IV.) For example, even if the camera is already properly aimed you could rotate it around its z axis. The most direct rotation is given by the nonunit quaternion q = [(b,a,0),1c], to aim z axis along unit vector (a,b,c) Normalization is advised, but it will fail for aim vector (0,0,1). In that case just rotate 180 degrees around the y axis, using q = [(0,1,0),0] If the camera is level after rotation by quaternion [(x,y,z),w], the y component of its rotated x axis will be zero, so xy+wz = 0 If it is upright, the y component of its rotated y axis will not be negative, so wwxx+yyzz >= 0 To ensure these two desirable properties, aim with a more sophisticated nonunit quaternion [(bs,at,ab),st], where s = rc, t = r+1, and r = sqrt(aa+cc). This can also fail to normalize, in which case normalize instead [(0,1+c,b),0] Unless the aim vector is null, this will succeed. If the aim vector has not been normalized and its magnitude is m = sqrt(aa+bb+cc), substitute 1>m. That is, use t = r+m and use m+c. More generally, to rotate unit vector U1 directly to unit vector U2, the nonunit quaternion will be q = [U1xU2,1+U1.U2] Why? If U is a unit vector, then it is normal to a plane through the origin with equation U.P = 0. Reflection in that plane is given by reversing the U component of P. reflect(P,U) = P ^Ö 2(U.P)U The quaternion product of U1 and U2 is [U1xU2,U1.U2], so 2 (U.P) = PU + UP Noting UU = 1, this gives a quaternion reflection formula. reflect(P,U) = P + (PU+UP)U = P ^Ö P + UPU = UPU Reflecting with U1 then U2, by U2(U1 P U1)U2, rotates by twice the angle between the planes, with axis perpendicular to both normals. Noting U1U2 is the conjugate of U2U1, and q rotates like q, the rotation quaternion is q = U2U1 = [U2xU1,U2.U1] = [U1xU2,U1.U2] This q fails to aim U1 at U2 by rotating twice as much as needed, but its square root succeeds. One square root of unit q is 1+q normalized, geometrically the bisection of the great arc from the identity to q. There is an inevitable singularity when U2 is the opposite of U1, because any perpendicular axis gives an equally direct 180 degree rotation. [These quaternion methods were provided by Ken Shoemake.]  Subject 5.27: How do I transform normals? In 3D, the orientation of a plane in space can be given by a vector perpendicular to the plane, a "normal vector" or "normal" for short. Often it is convenient to keep that vector of unit length, or "normalized"; be careful of the different meanings of "normality". A smooth surface has a plane tangent to each point, and by extension a normal to that plane is called a "surface normal". Graphics code also cheats by associating artificial normal vectors with the vertices of polygonal models to simulate the reflection properties of curved surfaces; these are called "vertex normals". The "orientation" of a plane has two meanings, both of which usually apply. Aside from the rotational tilting and turning meaning, there is also the sense of "side". A closed convex surface made of polygons has two sides, an inside and an outside, and normals can be assigned to the polygons in such a way that they all consistently point outside. This is often desirable for shading and culling. When a model is defined in one coordinate system and used in another, as is commonly done, it may be necessary to transform normals also. If the change of point coordinates is effected by means of a rotation plus a translation, one simply rotates the normals as well (with no translation). Other coordinate changes need more care. Although it is possible to use projective transformations to map a model into world coordinates, ordinarily they are used only for viewing. It is usually a mistake to apply perspective to a normal, as shading and culling are best done in world coordinates for correct results. Also perspective may be computed using degenerate matrices which are not invertible, though that need not be the case. For the importance of this, see below. The combination of a linear transformation and a translation is called an affine transformation, and is performed as a matrix multiplication plus a vector addition: p' = A(p) = Lp + t. When the modeltoworld point transform is affine, the proper way to transform normals is with the transpose of the inverse of L. n' = (L^{1})^T n However that is not enough. If L includes scaling effects, a unit normal in model space will usually transform to a nonunit normal, which can cause problems for shaders and other code. This may need correcting after the normal is transformed. If L includes reflection, the insideoutside orientation of the normal is reversed. This, too, can cause problems, and may need correcting. The determinant of L will be negative in this case. When a complicated distortion is used, it must be approximated differently at each point in space by a linear transform made up of partial derivatives, the Jacobian. The matrix for the Jacobian replaces L in the equation for transforming normals. Why use the transposed inverse? Write the dot product of column vectors n and v as a matrix product n^T v. Write vector v as a difference of points, qp. Let p, q, and thus v lie in the desired plane, and let n be normal to it. Vectors at right angles have zero dot product. n^T v = 0 The transform v' of v is v' = (Lq+t)(Lp+t) = (LqLp)+(tt) = L(qp) = Lv The transform n' of n will remain normal if it satisfies n'^T v' = n^T v Let n' = Mn for some M. Then the requirement is n'^T v' = (Mn)^T (Lv) = n^T (M^T L) v = n^T v This holds if M^T L = I where I is the identity. Right multiplying by the inverse L^{1} and transposing both sides gives, as claimed, M = (L^{1})^T When L is a rotation, L^{1} = L^T, so M = L. When L has no inverse it will still have an "adjoint" to substitute for for orthogonality purposes, differing only by a scale factor.  Section 6. Geometric Structures and Mathematics  Subject 6.01: Where can I get source for Voronoi/Delaunay triangulation? For 2d Delaunay triangulation, try Shewchuk's triangle program. It includes options for constrained triangulation and quality mesh generation. It uses exact arithmetic. The Delaunay triangulation is equivalent to computing the convex hull of the points lifted to a paraboloid. For nd Delaunay triangulation try Clarkson's hull program (exact arithmetic) or Barber and Huhdanpaa's Qhull program (floating point arithmetic). The hull program also computes Voronoi volumes and alpha shapes. The Qhull program also computes 2d Voronoi diagrams and nd Voronoi vertices. The output of both programs may be visualized with Geomview. There are many other codes for Delaunay triangulation and Voronoi diagrams. See Amenta's list of computational geometry software. Especially noteworthy is the CGAL code: Subject 0.07 and http://www.cgal.org. The Delaunay triangulation satisfies the following property: the circumcircle of each triangle is empty. The Voronoi diagram is the closestpoint map, i.e., each Voronoi cell identifies the points that are closest to an input site. The Voronoi diagram is the dual of the Delaunay triangulation. Both structures are defined for general dimension. Delaunay triangulation is an important part of mesh generation. There is a FAQ of polyhedral computation explaining how to compute nd Delaunay triangulation and nd Voronoi diagram using a convex hull code, and how to use the linear programming technique to determine the Voronoi cells adjacent to a given Voronoi cell efficiently for large scale or higher dimensional cases. Avis' lrs code uses the same file formats as cdd. It uses exact arithmetic and is useful for problems with very large output size, since it does not require storing the output. On a closely related topic, see http://www.cis.ohiostate.edu/~tamaldey/medialaxis.htm for computation of the 3D medial axis from the Voronoi diagram. References: Amenta: http://www.geom.umn.edu/software/cglist Avis: ftp://mutt.cs.mcgill.ca/pub/C/lrs.html Barber & http://www.geom.umn.edu/locate/qhull Huhdanpaa ftp://geom.umn.edu/pub/software/ ftp://ftp.geom.umn.edu/pub/software/ Clarkson: http://cm.belllabs.com/netlib/voronoi/hull.html ftp://cm.belllabs.com/netlib/voronoi/hull.shar.Z Geomview: http://www.geom.umn.edu/locate/geomview ftp://geom.umn.edu/pub/software/geomview/ Polyhedral Computation FAQ: http://www.ifor.math.ethz.ch/ifor/staff/fukuda/fukuda.html Shewchuk: http://www.cs.cmu.edu/~quake/triangle.html ftp://cm.belllabs.com/netlib/voronoi/triangle.shar.Z [O' Rourke (C)] pp. 168204 [Preparata & Shamos] pp. 204225 Chew, L. P. 1987. "Constrained Delaunay Triangulations," Proc. Third Annual ACM Symposium on Computational Geometry. Chew, L. P. 1989. "Constrained Delaunay Triangulations," Algorithmica 4:97108. (UPDATED VERSION OF CHEW 1987.) Fang, TP. and L. A. Piegl. 1994. "Algorithm for Constrained Delaunay Triangulation," The Visual Computer 10:255265. (RECOMMENDED!) Frey, W. H. 1987. "Selective Refinement: A New Strategy for Automatic Node Placement in Graded Triangular Meshes," International Journal for Numerical Methods in Engineering 24:21832200. Guibas, L. and J. Stolfi. 1985. "Primitives for the Manipulation of General Subdivisions and the Computation of Voronoi Diagrams," ACM Transactions on Graphics 4(2):74123. Karasick, M., D. Lieber, and L. R. Nackman. 1991. "Efficient Delaunay Triangulation Using Rational Arithmetic," ACM Transactions on Graphics 10(1):7191. Lischinski, D. 1994. "Incremental Delaunay Triangulation," Graphics Gems IV, P. S. Heckbert, Ed. Cambridge, MA: Academic Press Professional. (INCLUDES C++ SOURCE CODE  THANK YOU, DANI!) [Okabe] Schuierer, S. 1989. "Delaunay Triangulation and the Radiosity Approach," Proc. Eurographics '89, W. Hansmann, F. R. A. Hopgood, and W. Strasser, Eds. Elsevier Science Publishers, 345353. Subramanian, G., V. V. S. Raveendra, and M. G. Kamath. 1994. "Robust Boundary Triangulation and Delaunay Triangulation of Arbitrary Triangular Domains," International Journal for Numerical Methods in Engineering 37(10):17791789. Watson, D. F. and G. M. Philip. 1984. "Survey: Systematic Triangulations," Computer Vision, Graphics, and Image Processing 26:217223.  Subject 6.02: Where do I get source for convex hull? For nd convex hulls, try Clarkson's hull program (exact arithmetic) or Barber and Huhdanpaa's Qhull program (floating point arithmetic). Qhull 3.1 includes triangulated output and improved handling of difficult inputs. Qhull computes convex hulls, Delaunay triangulations, Voronoi diagrams, and halfspace intersections about a point. Qhull handles numeric precision problems by merging facets. The output of both programs may be visualized with Geomview. In higher dimensions, the number of facets may be much smaller than the number of lowerdimensional features. When this is the case, Fukuda's cdd program is much faster than Qhull or hull. There are many other codes for convex hulls. See Amenta's list of computational geometry software. References: Amenta: http://www.geom.umn.edu/software/cglist/ Barber & http://www.geom.umn.edu/locate/qhull Huhdanpaa Clarkson: http://cm.belllabs.com/netlib/voronoi/hull.html ftp://cm.belllabs.com/netlib/voronoi/hull.shar.Z Geomview: http://www.geom.umn.edu/locate/geomview ftp://geom.umn.edu/pub/software/geomview/ Fukuda: http://www.ifor.math.ethz.ch/staff/fukuda/cdd_home/cdd.html ftp://ftp.ifor.math.ethz.ch/pub/fukuda/cdd/ [O' Rourke (C)] pp. 70167 C code for Graham's algorithm on p.8096. Threedimensional convex hull discussed in Chapter 4 (p.11367). C code for the incremental algorithm on p.13060. [Preparata & Shamos] pp. 95184  Subject 6.03: Where do I get source for halfspace intersection? For nd halfspace intersection, try Fukuda's cdd program or Barber and Huhdanpaa's Qhull program. Both use floating point arithmetic. Fukuda includes code for exact arithmetic. Qhull handles numeric precision problems by merging facets. Qhull computes halfspace intersection by computing a convex hull. The intersection of halfspaces about the origin is equivalent to the convex hull of the halfspace coefficients divided by their offsets. See Subject 6.02 for more information. References: Barber & http://www.geom.umn.edu/locate/qhull Huhdanpaa Fukuda: ftp://ifor13.ethz.ch/pub/fukuda/cdd/ [Preparata & Shamos] pp. 315320  Subject 6.04: What are barycentric coordinates? Let p1, p2, p3 be the three vertices (corners) of a closed triangle T (in 2D or 3D or any dimension). Let t1, t2, t3 be real numbers that sum to 1, with each between 0 and 1: t1 + t2 + t3 = 1, 0 <= ti <= 1. Then the point p = t1*p1 + t2*p2 + t3*p3 lies in the plane of T and is inside T. The numbers (t1,t2,t3) are called the barycentric coordinates of p with respect to T. They uniquely identify p. They can be viewed as masses placed at the vertices whose center of gravity is p. For example, let p1=(0,0), p2=(1,0), p3=(3,2). The barycentric coordinates (1/2,0,1/2) specify the point p = (0,0)/2 + 0*(1,0) + (3,2)/2 = (3/2,1), the midpoint of the p1p3 edge. If p is joined to the three vertices, T is partitioned into three triangles. Call them T1, T2, T3, with Ti not incident to pi. The areas of these triangles Ti are proportional to the barycentric coordinates ti of p. Reference: [Coxeter, Intro. to Geometry, p.217].  Subject 6.05: How can I generate a random point inside a triangle? Use barycentric coordinates (see item 53) . Let A, B, C be the three vertices of your triangle. Any point P inside can be expressed uniquely as P = aA + bB + cC, where a+b+c=1 and a,b,c are each >= 0. Knowing a and b permits you to calculate c=1ab. So if you can generate two random numbers a and b, each in [0,1], such that their sum <=1, you've got a random point in your triangle. Generate random a and b independently and uniformly in [0,1] (just divide the standard C rand() by its max value to get such a random number.) If a+b>1, replace a by 1a, b by 1b. Let c=1ab. Then aA + bB + cC is uniformly distributed in triangle ABC: the reflection step a=1a; b=1b gives a point (a,b) uniformly distributed in the triangle (0,0)(1,0)(0,1), which is then mapped affinely to ABC. Now you have barycentric coordinates a,b,c. Compute your point P = aA + bB + cC. Reference: [Gems I], Turk, pp. 2428, contains a similar but different method which requires a square root.  Subject 6.06: How do I evenly distribute N points on (tesselate) a sphere? "Evenly distributed" doesn't have a single definition. There is a sense in which only the five Platonic solids achieve regular tesselations, as they are the only ones whose faces are regular and equal, with each vertex incident to the same number of faces. But generally "even distribution" focusses not so much on the induced tesselation, as it does on the distances and arrangement of the points/vertices. For example, eight points can be placed on the sphere further away from one another than is achieved by the vertices of an inscribed cube: start with an inscribed cube, twist the top face 45 degrees about the north pole, and then move the top and bottom points along the surface towards the equator a bit. The various definitions of "evenly distributed" lead into moderately complex mathematics. This topic happens to be a FAQ on sci.math as well as on comp.graphics.algorithms; a much more extensive and rigorous discussion written by Dave Rusin can be found at: http://www.math.niu.edu/~rusin/knownmath/95/sphere.faq A simple method of tesselating the sphere is repeated subdivision. An example starts with a unit octahedron. At each stage, every triangle in the tesselation is divided into 4 smaller triangles by creating 3 new vertices halfway along the edges. The new vertices are normalized, "pushing" them out to lie on the sphere. After N steps of subdivision, there will be 8 * 4^N triangles in the tesselation. A simple example of tesselation by subdivision is available at ftp://ftp.cs.unc.edu/pub/users/jon/sphere.c One frequently used definition of "evenness" is a distribution that minimizes a 1/r potential energy function of all the points, so that in a global sense points are as "far away" from each other as possible. Starting from an arbitrary distribution, we can either use numerical minimization algorithms or point repulsion, in which all the points are considered to repel each other according to a 1/r^2 force law and dynamics are simulated. The algorithm is run until some convergence criterion is satisfied, and the resulting distribution is our approximation. Jon Leech developed code to do just this. It is available at ftp://ftp.cs.unc.edu/pub/users/jon/points.tar.gz. See his README file for more information. His distribution includes sample output files for various n < 300, which may be used offtheshelf if that is all you need. Another method that is simpler than the above, but attains less uniformity, is based on spacing the points along a spiral that encircles the sphere. Code available from links at http://cs.smith.edu/~orourke/.  Subject 6.07: What are coordinates for the vertices of an icosohedron? Data on various polyhedra is available at http://cm.belllabs.com/netlib/polyhedra/index.html, or http://netlib.belllabs.com/netlib/polyhedra/index.html, or http://www.netlib.org/polyhedra/index.html For the icosahedron, the twelve vertices are: (+ 1, 0, +t), (0, +t, +1), and (+t, +1, 0) where t = (sqrt(5)1)/2, the golden ratio. Reference: "Beyond the Third Dimension" by Banchoff, p.168.  Subject 6.08: How do I generate random points on the surface of a sphere? There are several methods. Perhaps the easiest to understand is the "rejection method": generate random points in an origin centered cube with opposite corners (r,r,r) and (r,r,r). Reject any point p that falls outside of the sphere of radius r. Scale the vector to lie on the surface. Because the cube to sphere volume ratio is pi/6, the average number of iterations before an acceptable vector is found is 6/pi = 1.90986. This essentially doubles the effort, and makes this method slower than the "trig method." A timing comparison conducted by Ken Sloan showed that the trig method runs in about 2/3's the time of the rejection method. He found that methods based on the use of normal distributions are twice as slow as the rejection method. The trig method. This method works only in 3space, but it is very fast. It depends on the slightly counterintuitive fact (see proof below) that each of the three coordinates is uniformly distributed on [1,1] (but the three are not independent, obviously). Therefore, it suffices to choose one axis (Z, say) and generate a uniformly distributed value on that axis. This constrains the chosen point to lie on a circle parallel to the XY plane, and the obvious trig method may be used to obtain the remaining coordinates. (a) Choose z uniformly distributed in [1,1]. (b) Choose t uniformly distributed on [0, 2*pi). (c) Let r = sqrt(1z^2). (d) Let x = r * cos(t). (e) Let y = r * sin(t). This method uses uniform deviates (faster to generate than normal deviates), and no set of coordinates is ever rejected. Here is a proof of correctness for the fact that the zcoordinate is uniformly distributed. The proof also works for the x and y coordinates, but note that this works only in 3space. The area of a surface of revolution in 3space is given by A = 2 * pi * int_a^b f(x) * sqrt(1 + [f'(x}]^2) dx Consider a zone of a sphere of radius R. Since we are integrating in the z direction, we have f(z) = sqrt(R^2  z^2) f'(z) = z / sqrt(R^2z^2) 1 + [f'(z)]^2 = r^2 / (R^2z^2) A = 2 * pi * int_a^b sqrt(R^2z^2) * R/sqrt(R^2z^2) dz = 2 * pi * R int_a^b dz = 2 * pi * R * (ba) = 2 * pi * R * h, where h = ba is the vertical height of the zone. Notice how the integrand reduces to a constant. The density is therefore uniform. Here is simple C code implementing the trig method: void SpherePoints(int n, double X[], double Y[], double Z[]) { int i; double x, y, z, w, t; for( i=0; i< n; i++ ) { z = 2.0 * drand48()  1.0; t = 2.0 * M_PI * drand48(); w = sqrt( 1  z*z ); x = w * cos( t ); y = w * sin( t ); printf("i=%d: x,y,z=\t%10.5lf\t%10.5lf\t%10.5lf\n", i, x,y,z); X[i] = x; Y[i] = y; Z[i] = z; } } A complete package is available at ftp://cs.smith.edu/pub/code/sphere.tar.gz (4K), reachable from http://cs.smith.edu/~orourke/ . If one wants to generate the random points in terms of longitude and latitude in degrees, these equations suffice: Longitude = random * 360  180 Latitude = [arccos( random * 2  1 )/pi ] * 180  90 References: [Knuth, vol. 2] [Graphics Gems IV] "Uniform Random Rotations"  Subject 6.09: What are Plucker coordinates? A common convention is to write umlauted u as "ue", so you'll also see "Pluecker". Lines in 3D can easily be given by listing the coordinates of two distinct points, for a total of six numbers. Or, they can be given as the coordinates of two distinct planes, eight numbers. What's wrong with these? Nothing; but we can do better. Pluecker coordinates are, in a sense, halfway between these extremes, and can trivially generate either. Neither extreme is as efficient as Pluecker coordinates for computations. When Pluecker coordinates generalize to Grassmann coordinates, as laid out beautifully in [Hodge], Chapter VII, the determinant definition is clearly the one to use. But 3D lines can use a simpler definition. Take two distinct points on a line, say P = (Px,Py,Pz) Q = (Qx,Qy,Qz) Think of these as vectors from the origin, if you like. The Pluecker coordinates for the line are essentially U = P  Q V = P x Q Except for a scale factor, which we ignore, U and V do not depend on the specific P and Q! Cross products are perpendicular to their factors, so we always have U.V = 0. In [Stolfi] lines have orientation, so are the same only if their Pluecker coordinates are related by a positive scale factor. As determinants of homogeneous coordinates, begin with the 4x2 matrix [ Px Qx ] row x [ Py Qy ] row y [ Pz Qz ] row z [ 1 1 ] row w Define Pluecker coordinate Gij as the determinant of rows i and j, in that order. Notice that Giw = Pi  Qi, which is Ui. Now let (i,j,k) be a cyclic permutation of (x,y,z), namely (x,y,z) or (y,z,x) or (z,x,y), and notice that Gij = Vk. Determinants are antisymmetric in the rows, so Gij = Gji. Thus all possible Pluecker line coordinates are either zero (if i=j) or components of U or V, perhaps with a sign change. Taking the w component of a vector as 0, the determinant form will operate just as well on a point P and vector U as on two points. We can also begin with a 2x4 matrix whose rows are the coefficients of homogeneous plane equations, E.P=0, from which come dual coordinates G'ij. Now if (h,i,j,k) is an even permutation of (x,y,z,w), then Ghi = G'jk. (Just swap U and V!) Got Pluecker, want points? No problem. At least one component of U is nonzero, say Ui, which is Giw. Create homogeneous points Pj = Gjw + Gij, and Qj = Gij. (Don't expect the P and Q that we started with, and don't expect w=1.) Want plane equations? Let (i,j,k,w) be an even permutation of (x,y,z,w), so G'jk = Giw. Then create Eh = G'hk, and Fh = G'jh. Example: Begin with P = (2,4,8) and Q = (2,3,5). Then U = (0,1,3) and V = (4,6,2). The direct determinant forms are Gxw=0, Gyw=1, Gzw=3, Gyz=4, Gzx=6, Gxy=2, and the dual forms are G'yz=0, G'zx=1, G'xy=3, G'xw=4, G'yw=6, G'zw=2. Take Uz = Gzw = G'xy = 3 as a suitable nonzero element. Then two planes meeting in the line are (G'xy G'yy G'zy G'wy).P = 0 (G'xx G'xy G'xz G'xw).P = 0 That is, a point P is on the line if it satisfies both these equations: 3 Px + 0 Py + 0 Pz  6 Pw = 0 0 Px + 3 Py  1 Pz  4 Pw = 0 We can also easily determine if two lines meet, or if not, how they pass. If U1 and V1 are the coordinates of line 1, U2 and V2, of line 2, we look at the sign of U1.V2 + V1.U2. If it's zero, they meet. The determinant form reveals even permutations of (x,y,z,w): G1xw G2yz + G1yw G2zx + G1zw G2xy + G1yz G2xw + G1zx p2yw + G1xy p2zw Two oriented lines L1 and L2 can interact in three different ways: L1 might intersect L2, L1 might go clockwise around L2, or L1 might go counterclockwise around L2. Here are some examples:  L2  L2  L2 L1  L1  L1  +> > >    V V V intersect counterclockwise clockwise  L2  L2  L2 L1  L1  L1  <+ < <    V V V The first and second rows are just different views of the same lines, once from the "front" and once from the "back." Here's what they might look like if you look straight down line L2 (shown here as a dot). L1 > o> L1 o L1 o > intersect counterclockwise clockwise The Pluecker coordinates of L1 and L2 give you a quick way to test which of the three it is. cw: U1.V2 + V1.U2 < 0 ccw: U1.V2 + V1.U2 > 0 thru: U1.V2 + V1.U2 = 0 So why is this useful? Suppose you want to test if a ray intersects a triangle in 3space. One way to do this is to represent the ray and the edges of the triangle with Pluecker coordinates. The ray hits the triangle if and only if it hits one of the triangle's edges, or it's "clockwise" from all three edges, or it's "counterclockwise" from all three edges. For example... o _  \ ...in this picture, the ray  \ is oriented counterclockwise  \ > from all three edges, so it  \ must intersect the triangle. v \ o> o Using Pluecker coordinates, ray shooting tests like this take only a few lines of code. Grassmann coordinates allow similar methods to be used for points, lines, planes, and so on, and in a space of any dimension. In the case of lines in 2D, only three coordinates are required: Gxw = PxQx = G'y Gyw = PyQy = G'x Gxy = PxQyPyQx = G'w Since P and Q are distinct, Giw is nonzero for i = x or y. Then (Gix,Giy,Giw) is a point P1 on L (Gxw,Gyw,Gww)+P1 is a point P2 on L (G'x,G'y,G'w).P = 0 is an equation for L Two lines in a 2D perspective plane always meet, perhaps in an ideal point (meaning they're parallel in ordinary 2D). Calling their homogeneous point of intersection P, the computation from Grassmann coordinates nicely illustrates the convenience. (See Subj 1.03, "How do I find intersections of 2 2D line segments?") For i = x,y,w, set Pi = G'j H'k ^Ö G'k H'j, where (i,j,k) is even See [Hodge] for a thorough discussion of the theory, [Stolfi] for a little theory with a concise implementation for low dimensions (see Subj. 0.04), and these articles for further discussion: J. Erickson, Pluecker Coordinates, Ray Tracing News 10(3) 1997, http://www.acm.org/tog/resources/RTNews/html/rtnv10n3.html. Ken Shoemake, Pluecker Coordinate Tutorial, Ray Tracing News 11(1) 1998, http://www.acm.org/tog/resources/RTNews/html/rtnv11n1.html.  Section 7. Contributors  Subject 7.01: How can you contribute to this FAQ? Send email to orourke@cs.smith.edu with your suggestions, possible topics, corrections, or pointers to information.  Subject 7.02: Contributors. Who made this all possible. [All email addresses now removed to protect the authors from spam.] Jens Alfke Nina Amenta Leen Ammeraal Scott Anguish Ian Ashdown Barak Brad Barber James Beech David Bouman Paul Bourke Lars Brinkhoff Andrew Bromage Brent Burley R. Kevin Burton Gene Caldwell Ken Clarkson Robert Day Tamal Dey Martin Dillon Thomas Djafari Dave Eberly John Eickemeyer John E (Edward) Ellis Jeff Erickson Ata Etemadi Hugh Fisher David N. Fogel Arne K. Frick Olexandr Frantchuk Robert W. Fuentes Komei Fukuda William Gibbons Normand Grégoire Eric Haines Jeff Hameluck Sandy Harris Luiz Henrique de Figueiredo Steve Hollasch Bill Jones Richard Kinch Craig Kolb Uffe Kousgaard Stefan Krause Piyush Kumar Steve Lamont Ben Landon Erik Larsen Jon Leech Michael V. Leonov Sum Lin Alan J. Livingston Sebastien Loisel Fritz Lott Jacob Marner Marc Christopher Martin John McNamara Samuel Murphy Alan Murta S. N. Muthukrishnan Andrew Myers David Nixon Aaron Orenstein Joseph O'Rourke Samuel S. Paik Leonidas Palios Amitha Perera Brian Peters Lavoie Philippe Christopher Phillips Tom Plunket Aaron Quigley Rudi Bjxrn Rasmussen Greg Roelofs Christian von Roques Dave Seaman Jonathan R. Shewchuk Rainer Michael Schmid Klamer Schutte Andrzej Serafin ZhengYu Shan James Sharman Ken Shoemake Jeff Somers Jon Stone Dan Sunday Seth Teller Saurabh Tendulkar Yael "YoeL" Touboul Anson Tsao Bob van Manen Remco Veltkamp Jim Ward Jason Weiler Karsten Weiss Stefan Wolfrum Daniel S. Zwick Previous Editors: Jon Stone Anson Tsao User Contributions:Comment about this article, ask questions, or add new information about this topic:
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