Patent application number | Description | Published |
20140320492 | METHODS AND APPARATUS FOR REFLECTIVE SYMMETRY BASED 3D MODEL COMPRESSION - Encoders and decoders, and methods of encoding and decoding, are provided for rendering 3D images. The 3D images are decomposed by analyzing components of the 3D images to match reflections of patterns in the 3D images, and to restore the components for further rendering of the 3D image. The encoders and decoders utilize principles of reflective symmetry to effectively match symmetrical points in an image so that the symmetrical points can be characterized by a rotation and translation matrix, thereby reducing the requirement of coding and decoding all of the points in 3D image and increasing computational efficiency. | 10-30-2014 |
20140324914 | POSITION CODING BASED ON SPATIAL TREE WITH DUPLICATE POINTS - A method and an apparatus for constructing a spatial tree data structure corresponding to a region. According to the present principles, a cell may include therein a point or a set of points that are determined to be duplicate points. In an embodiment the duplicate points are determined based on the size of the points included within the cell The inclusion of duplicate points within a particular cell, rather than further subdividing the cell, provides coding efficiency. The present principles are particularly advantageous in the context of quadtree or octree type partitioning, and may be used in 3D mesh coding. | 10-30-2014 |
20140334717 | METHOD AND APPARATUS FOR COMPRESSING TEXTURE INFORMATION OF THREE-DIMENSIONAL (3D) MODELS - A 3D model can be modeled using “pattern-instance?representation. To describe the vertices and triangles, properties of the instance, for example, texture, color, and normal, are adjusted to correspond to the order in the pattern. The texture of an instance is encoded depending on its similarity with the texture of a corresponding pattern. When instance texture is identical or almost identical to the pattern texture, the instance texture is not encoded and the pattern texture will be used to reconstruct the instance texture. When the instance texture is similar to the pattern texture, the instance texture is predictively encoded from the pattern texture, that is, the difference between the instance texture and pattern texture is encoded, and the instance texture is determined as a combination of the pattern texture and the difference. | 11-13-2014 |
20140340393 | SYSTEM AND METHOD FOR ERROR CONTROLLABLE REPETITIVE STRUCTURE DISCOVERY BASED COMPRESSION - A method and an apparatus for 3D model compression are described. Repetitive structures in the 3D model are identified to increase the compression ratio by reducing the redundancy among the instance components. The instance components can be expressed in a “pattern-instance” representation and a decision is made as to whether to compress the “pattern-instance” representation for the 3D model. For those instance components that are determined to be encoded in “pattern-instance” representation, a verification process is employed to examine the decoding error of the instance components. If the decoding error is below a threshold value, the instance components are compressed in the “pattern-instance” representation. Otherwise, a different encoding mode is used to compress the instance components. | 11-20-2014 |
20150009211 | METHOD FOR SETTING AND DETERMINING DIRECTIONS OF PRINCIPAL AXES OF 3D OBJECT - The invention provides a method for setting the directions of principal axes of a 3D object is provided. The method comprises: for each of any two principal axes, setting the direction of the principal axis according to at least one predefined function, with which the result calculated of the 3D object for the vertices in the positive half space of the principal axis is smaller than or equal to the result for the vertices in the negative half space of the principal axis, wherein a vertex in the positive half space of the principal axis means the one with a coordinate of the principal axis larger than 0, and a vertex in the negative half space of the principal axis means the one with a coordinate of the axis smaller than 0; setting the direction of the third principal axis of to follow the right-hand rule with said two principal axes, wherein the vector for the third axis is the cross product of the vectors for said two principal axes; and displaying a signal of the 3D object with the directions of the principal axes set according to the above steps. | 01-08-2015 |
20150016742 | METHODS FOR COMPENSATING DECODING ERROR IN THREE-DIMENSIONAL MODELS - Encoders compress 3D images and compensate for decoding error using instance component decoders which decode instance components of the 3D image to generate decoded instance components, error calculation units which compare the decoded instance components with corresponding uncompressed instance components to calculate decoding errors, and determination units which determine if the encoded components pass a verification according to a threshold based on the decoding errors. | 01-15-2015 |
20150084953 | METHOD AND APPARATUS FOR ESTIMATING ERROR METRICS FOR MULTI-COMPONENT 3D MODELS - To calculate an error metric between two 3D multi-components models, the facets of 3D components of the first 3D model are uniformly sampled. Between each sampling point in the first 3D model and the surface of the second 3D model, a point-to-surface error is calculated. The point-to-surface errors are then processed to generate the error metric between the first and second 3D models. To speed up computation, the second 3D model can be partitioned into cells, and only the closet cell to a particular sampling point in the first 3D model is used to calculate the point-to-surface error, when computing error or metrics for individual 3D components in the 3D models, the same uniform sampling and cell partition are employed. Consequently, the error of the whole 3D model is substantially a weighted average of the errors computed for the individual components. | 03-26-2015 |
20150103074 | METHOD AND APPARATUS FOR GENERATING SHAPE DESCRIPTOR OF A MODEL - The invention provides a method for generating an n dimensional vector as a shape descriptor of a model, and corresponding apparatus and shape descriptor. The method comprises: determining a type element of the vector to describe the basic shape of the model; and calculating n−1 metric elements of the vector, each of which represents the percentage of all of a feature of the model falling into one of n−1 layers divided as a function of the type element. | 04-16-2015 |
20150221131 | METHOD AND APPARATUS FOR 3D MESH DE-NOISING - Disclosed are a method and apparatus for processing a 3D model. To preserve fine structures while de-noising a 3D mesh model, the local structural information around a vertex is captured when designing a de-noising filter. In particular, for a current vertex to be pro cessed, a path, for example, a geodesic path, is determined between the current vertex and each neighboring vertex. For each mesh edge along the path, local variations are calculated for the two end vertices of the mesh edge using a covariance matrix, and a geometric vari ation for the mesh edge is calculated as the difference between the two local variations. Then structural information for the region between the current vertex and a neighboring vertex is calculated as a function of the geometric variations for mesh edges along the path, for example, as the maximum geometric variation along the path. The present principles can also be adjusted to be used in de-noising 3D point-based models. | 08-06-2015 |
20150302115 | METHOD AND APPARATUS FOR CREATING 3D MODEL - The present invention provides a method and apparatus for creating a 3D model from an input 3D model based on repetitive structures of the input 3D model. The Method comprises: detecting repetitive structures of the at least one component of the input 3D model and hierarchical relationship of the repetitive structures, each repetitive structure comprising a component set as a representative and one or more components, if any, set as an instance; determining a relative transformation among all representatives and instances of all the repetitive structures of the input 3D model; and updating relevant components of the input 3D model according to a change of a representative in the repetitive structures and recalculating the poses of the updated components as a function of the determined relative transformation to create the 3D model. | 10-22-2015 |