Sparse tensor-based bitwise deep octree coding
AssigneeInterDigital VC Holdings, Inc.
InventorsJiahao PANG, Muhammad Asad LODHI, Dong TIAN
Life of this patent
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Abstract
In one implementation, we propose a bitwise octree coding approach based on deep neural networks and operations on 3D sparse tensors. To encode/decode a certain level of detail (LoD) in an octree, geometric features are first inherited from the previous LoD by upsampling. Then based on the already encoded/decoded voxels, the point cloud geometry is firstly refined by pruning, followed by combining with the known context information. In the end, feature aggregation and probability estimation can be applied to obtain the occupancy probabilities for actual arithmetic encoding/decoding. A corresponding probabilistic training strategy is also proposed for our bitwise octree coding approach.
Claims21
A method of encoding point cloud data, comprising:
- obtaining features associated with point cloud data for a point cloud, said point cloud data is represented as a sparse tensor at a level of detail (LoD);
- processing said features associated with said LoD to match a resolution of another LoD, wherein said another LoD is finer and subsequent to said LoD;
- for each occupied voxel in said LoD, encoding a plurality of voxels at said another LoD based on said processed features to obtain occupancy information at said another LoD;
- and
- updating said processed features, based on occupancy information at said another LoD, to generate updated features associated with said another LoD.
6-16. (canceled)
A method of decoding point cloud data, comprising:
- obtaining features associated with point cloud data for a point cloud, said point cloud data is represented as a sparse tensor at a level of detail (LoD);
- processing said features associated with said LoD to match a resolution of another LoD, wherein said another LoD is finer and subsequent to said LoD;
- for each occupied voxel in said LoD, decoding a plurality of voxels at said another LoD based on said processed features to obtain occupancy information at said another LoD;
- and
- updating said processed features, based on occupancy information at said another LoD, to generate updated features associated with said another LoD.
An apparatus for encoding point cloud data, comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to:
- obtain features associated with point cloud data for a point cloud, said point cloud data is represented as a sparse tensor at a level of detail (LoD);
- process said features associated with said LoD to match a resolution of another LoD, wherein said another LoD is finer and subsequent to said LoD;
- for each occupied voxel in said LoD, encode a plurality of voxels at said another LoD based on said processed features to obtain occupancy information at said another LoD;
- and
- update said processed features, based on occupancy information at said another LoD, to generate updated features associated with said another LoD.
An apparatus for decoding point cloud data, comprising one or more processors and at least one memory coupled to said one or more processors, wherein said one or more processors are configured to:
- obtain features associated with point cloud data for a point cloud, said point cloud data is represented as a sparse tensor at a level of detail (LoD);
- process said features associated with said LoD to match a resolution of another LoD, wherein said another LoD is finer and subsequent to said LoD;
- for each occupied voxel in said LoD, decode a plurality of voxels at said another LoD based on said processed features to obtain occupancy information at said another LoD;
- and
- update said processed features, based on occupancy information at said another LoD, to generate updated features associated with said another LoD.
Dependencies are inferred from the claim text.