Abstract: Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.

Abstract: Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.

Abstract: Techniques of compressing level of detail (LOD) data involve defining a cost metric that predicts how much computing resources are necessary to decode and render a mesh at a given LOD. The cost metric may be optimized by a selection of a LOD reduction process of a plurality of processes at each LOD reduction step. For each process of the plurality of processes, the LOD is reduced according to that process and the resulting reduced LOD is evaluated according to the cost metric. Each such process at that LOD reduction step produces a respective LOD, which includes a mesh, one or more texture atlases, and/or other attributes. The LOD produced by the process having the lowest value of the cost metric at a reduction step is the LOD that is input into the next LOD reduction step.

Abstract: An encoder may perform a method of compressing texture coordinates using texture atlas. In one example implementation, the method may include predicting texture coordinates of a corner of a triangle, the triangle being one of a plurality of triangles of a geometric mesh, the predicting based on a corresponding texture atlas and local information associated with the corner. The method further includes determining a residual vector based on the predicted texture coordinates, performing entropy encoding of the residual vector along with residual vectors of other corners of the geometric mesh, and generating compressed data based on the entropy encoding.

Abstract: Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.

Abstract: Techniques of compressing level of detail (LOD) data involve defining a cost metric that predicts how much computing resources are necessary to decode and render a mesh at a given LOD. The cost metric may be optimized by a selection of a LOD reduction process of a plurality of processes at each LOD reduction step. For each process of the plurality of processes, the LOD is reduced according to that process and the resulting reduced LOD is evaluated according to the cost metric. Each such process at that LOD reduction step produces a respective LOD, which includes a mesh, one or more texture atlases, and/or other attributes. The LOD produced by the process having the lowest value of the cost metric at a reduction step is the LOD that is input into the next LOD reduction step.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining priority values associated with collapse of each edge of a plurality of edges. The method may further include selecting a first edge from the plurality of edges, determining adjusted priority values of edges in a vicinity of the selected first edge, selecting a second edge from remaining edges of the plurality of edges after the selecting of the first edge, and collapsing the selected edges such that vertex split information is generated that is based on the collapsing of the selected edges. In some implementations, the method may further include entropy encoding of the vertex split information.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining a priority value for each edge of a plurality of edges, the priority value of an edge of the plurality of edges determined based on an error metric value and an estimated encoding cost associated with the edge. The method may further include determining a set of edges for collapse, the set of edges determined from the plurality of edges based on the priority values and collapsing the set of edges and generating vertex split information. In some implementations, the method may include entropy encoding the vertex split information.

Abstract: An encoder includes a processor and a memory. The encoder generates a first plurality of levels of detail (LODs) and associated first type of vertex split records, each of the first type of vertex split records associated with an LOD of the first plurality of LODs is generated using a first type of collapse operator. The encoder initiates a switch from using the first type of collapse operator to a second type of collapse operator in response to a switching condition being satisfied. The encode further a second plurality of LODs and associated second type of vertex split records, each of the second type of vertex split records associated with a LOD of the second plurality of LODs is generated using the second type of collapse operator.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include identifying a pair of partitioning vertices to be connected to a split vertex associated with a collapse of an edge, creating the split vertex by collapsing the edge, encoding partitioning vertex information associated with the pair of partitioning vertices, the encoding of the partitioning vertex information based on an ordering of vertices of an umbrella of the split vertex, and the ordering of vertices of the umbrella determined based on a geometric shape and connectivity of the umbrella, and generating vertex split information that includes the partitioning vertex information. In another example implementation, the method may include entropy encoding the vertex split information prior to being transmitted.

Abstract: An encoder may perform a method of compressing texture coordinates using texture atlas. In one example implementation, the method may include predicting texture coordinates of a corner of a triangle, the triangle being one of a plurality of triangles of a geometric mesh, the predicting based on a corresponding texture atlas and local information associated with the corner. The method further includes determining a residual vector based on the predicted texture coordinates, performing entropy encoding of the residual vector along with residual vectors of other corners of the geometric mesh, and generating compressed data based on the entropy encoding.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining a priority value for each edge of a plurality of edges, the priority value of an edge of the plurality of edges determined based on an error metric value and an estimated encoding cost associated with the edge. The method may further include determining a set of edges for collapse, the set of edges determined from the plurality of edges based on the priority values and collapsing the set of edges and generating vertex split information. In some implementations, the method may include entropy encoding the vertex split information.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include identifying a pair of partitioning vertices to be connected to a split vertex associated with a collapse of an edge, creating the split vertex by collapsing the edge, encoding partitioning vertex information associated with the pair of partitioning vertices, the encoding of the partitioning vertex information based on an ordering of vertices of an umbrella of the split vertex, and the ordering of vertices of the umbrella determined based on a geometric shape and connectivity of the umbrella, and generating vertex split information that includes the partitioning vertex information. In another example implementation, the method may include entropy encoding the vertex split information prior to being transmitted.

Abstract: An encoder includes a processor and a memory. The encoder may perform a method of progressive compression. In one example implementation, the method may include determining priority values associated with collapse of each edge of a plurality of edges. The method may further include selecting a first edge from the plurality of edges, determining adjusted priority values of edges in a vicinity of the selected first edge, selecting a second edge from remaining edges of the plurality of edges after the selecting of the first edge, and collapsing the selected edges such that vertex split information is generated that is based on the collapsing of the selected edges. In some implementations, the method may further include entropy encoding of the vertex split information.

Abstract: An encoder includes a processor and a memory. The encoder generates a first plurality of levels of detail (LODs) and associated first type of vertex split records, each of the first type of vertex split records associated with an LOD of the first plurality of LODs is generated using a first type of collapse operator. The encoder initiates a switch from using the first type of collapse operator to a second type of collapse operator in response to a switching condition being satisfied. The encode further a second plurality of LODs and associated second type of vertex split records, each of the second type of vertex split records associated with a LOD of the second plurality of LODs is generated using the second type of collapse operator.

Abstract: The processing of geometric data starts with a tolerance volume ? that is related to raw geometric starting data. A canonical triangulation is initiated (201) in the tolerance volume, on the basis of a set S of sample points taken at the limits of this tolerance volume, and points LBB located on the exterior thereof. This canonical triangulation is refined (203) until the sample points have been classified, thereby obtaining a dense mesh of the tolerance volume. This dense mesh is then simplified (207) via triangulation modification operations, while preserving the topology and classification of the sample points.

Abstract: A method for watermarking a three-dimensional object is disclosed. The three-dimensional object is represented by a mesh. A mesh comprises a plurality of vertices. The method further comprises computing an original thickness signature for said mesh from a plurality of thickness values, wherein a thickness value is computed for a vertex of the mesh; determining a target thickness signature, wherein the target thickness signature is a function of a watermark payload and of the original thickness signature; and modifying a position of at least one vertex of the mesh wherein a thickness signature computed for the modified mesh reaches the target thickness signature and wherein a distortion constraint between the mesh and the modified mesh is satisfied. A method for detecting a watermark in a three-dimensional object, a three-dimensional object carrying a watermark and, devices for implementing the methods are further disclosed.

Abstract: A method for watermarking a three-dimensional object is disclosed. The watermarking method comprises computing shape descriptor of a local neighborhood of a current vertex among the plurality of vertices of the three-dimensional object; obtaining a target shape descriptor from the shape descriptor using a quantization grid associated with a watermark payload; and modifying said local neighborhood wherein a position of at least one vertex of said local neighborhood is modified such that a shape descriptor of said modified local neighborhood is close to said target shape descriptor and wherein said current vertex is not modified. A method for obtaining payload from a three-dimensional object, a 3D object carrying a watermark and devices implementing the disclosed methods are further disclosed.

Abstract: A method for inserting features into a three-dimensional object is disclosed, wherein features correspond to original features of the three-dimensional object. The inserting method comprises determining a reference shape using the original features; determining a set of vertices on the three-dimensional original object at the intersection between the reference shape and the three-dimensional object; modifying the local neighborhood of the set of vertices so that their local neighborhood is close to a set of target shapes. A method for obtaining features from a three-dimensional object is further disclosed. The method comprises obtaining anchor vertices whose local neighborhood is close to a set of target shapes; fitting a reference shape onto the anchor vertices; and obtaining the features using the fitted reference shape. A 3D object carrying anchor vertices and devices for implementing the disclosed methods are further disclosed.

Abstract: A method for watermarking a three-dimensional object is disclosed. The three-dimensional object is represented by a mesh. A mesh comprises a plurality of vertices. The method further comprises computing an original thickness signature for said mesh from a plurality of thickness values, wherein a thickness value is computed for a vertex of the mesh; determining a target thickness signature, wherein the target thickness signature is a function of a watermark payload and of the original thickness signature; and modifying a position of at least one vertex of the mesh wherein a thickness signature computed for the modified mesh reaches the target thickness signature and wherein a distortion constraint between the mesh and the modified mesh is satisfied. A method for detecting a watermark in a three-dimensional object, a three-dimensional object carrying a watermark and, devices for implementing the methods are further disclosed.