Abstract: A system compresses and decompresses attribute information for visual volumetric content, such as a mesh representation. Attribute values are included in the visual volumetric representation, wherein at least some of the attribute values include unitary vectors, such as surface normal vectors or surface tangent vectors having a magnitude of one unit. In order to compress the attribute information the three-dimensional unit vectors are mapped into two dimensional parametric coordinates for a planar representation of a unit sphere. To reduce negative effects on compression due to distortion or discontinuities in the planar representation, mappings for compressing respective unit vectors are adaptively selected.

Abstract: A method of decomposing a three-dimensional representation of an object into a plurality of convex hulls can include instantiating a cluster priority queue in a computing system memory that initially contains a cluster corresponding to the three-dimensional representation of the object, computing with a processor of the computing system a concavity measure for each cluster in the cluster priority queue, and, for the cluster with the highest concavity measure: (1) computing with the processor a cut plane that divides the cluster corresponding to the three-dimensional representation of the object into two new clusters, each of the two new clusters having a corresponding convex hull, wherein computing a cut plane includes performing a hierarchical search of potential cut planes, (2) removing the cluster corresponding to the three-dimensional representation of the object from the cluster priority queue, and (3) adding the two new clusters to the cluster priority queue.

Abstract: A system compresses and decompresses attribute information for visual volumetric content, such as a mesh representation. Attribute values are included in the visual volumetric representation, wherein at least some of the attribute values include unitary vectors, such as surface normal vectors or surface tangent vectors having a magnitude of one unit. In order to compress the attribute information the three-dimensional unit vectors are mapped into two dimensional parametric coordinates for a planar representation of a unit sphere. To reduce negative effects on compression due to distortion or discontinuities in the planar representation, mappings for compressing respective unit vectors are adaptively selected.

Abstract: A system compresses and decompresses attribute information for visual volumetric content, such as a mesh representation. Attribute values are included in the visual volumetric representation, wherein at least some of the attribute values include unitary vectors, such as surface normal vectors or surface tangent vectors having a magnitude of one unit. In order to compress the attribute information the three-dimensional unit vectors are mapped into two dimensional parametric coordinates for a planar representation of a unit sphere. To reduce negative effects on compression due to distortion or discontinuities in the planar representation, mappings for compressing respective unit vectors are adaptively selected.

Abstract: A method of decomposing a three-dimensional representation of an object into a plurality of convex hulls can include instantiating a cluster priority queue in a computing system memory that initially contains a cluster corresponding to the three-dimensional representation of the object, computing with a processor of the computing system a concavity measure for each cluster in the cluster priority queue, and, for the cluster with the highest concavity measure: (1) computing with the processor a cut plane that divides the cluster corresponding to the three-dimensional representation of the object into two new clusters, each of the two new clusters having a corresponding convex hull, wherein computing a cut plane includes performing a hierarchical search of potential cut planes, (2) removing the cluster corresponding to the three-dimensional representation of the object from the cluster priority queue, and (3) adding the two new clusters to the cluster priority queue.