Patent №
US 9,035,807
Granted
2015-05-19
Filed 2014
Owner
THOMSON LICENSING
Lab
—
AI components
1
vision
Assignment
Recorded
Dataset
AIPD
2023_r1 edition
Application
14240066
A particular implementation receives geometry data of a 3D mesh, and represents the geometry data with an octree. The particular implementation partitions the octree into three parts, wherein the symbols corresponding to the middle part of the octree are hierarchical entropy encoded. To partition the octree into three parts, different thresholds are used. Depending on whether a symbol associated with a node is an S1 symbol, the child node of the node is included in the middle part or the upper part of the octree. In hierarchical entropy encoding, a non-S1 symbol is first encoded as a pre-determined symbol ‘X’ using symbol set S2={S1, ‘X’} and the non-S1 symbol itself is then encoded using symbol set S0 (S2⊂S0), and an S1 symbol is encoded using symbol set S2. Another implementation defines corresponding hierarchical entropy decoding. A further implementation reconstructs the octree and restores the geometry data of a 3D mesh from the octree representation.
AI classification
Ownership
THOMSON LICENSING
assignment · 329230405
Assignors
JIANG, WENFEI, CAI, KANGYING, HU, PING
On an employer assignment, the assignors are typically the inventors.