Sparse Tensor Octree Coding for Low-Cost 3D Point Cloud Compression
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Solution Overview
Problem
Existing technologies struggle with processing and compressing 3D point clouds data, particularly in the automotive, virtual, and context of the automotive, virtual, and context of the automotive, virtual, and context of the automotive, virtual, and context of the point clouds, are inefficient and computationally expensive, especially for consumer devices with limited computational power.
Innovation Solution
A method and apparatus for encoding and decoding point cloud data using a sparse tensor format, employing bitwise octree coding with context modeling and deep learning to efficiently compress and process point clouds, leveraging sparse tensor operators and neural networks for occupancy probability estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional methods are used to process and compress 3D point cloud data, then processing capability is maintained, but computational cost and energy consumption are excessively high
Solution Approach 1:
The patent divides the 3D point cloud data into multiple levels of detail (LoD) using octree decomposition, where the point cloud is segmented into parent voxels and child voxels across different resolution levels. This segmentation allows processing to focus on occupied regions only, reducing computational cost while maintaining processing capability for the entire point cloud structure.
Solution Approach 2:
The patent applies different processing strategies to different regions of the point cloud based on occupancy information. Occupied voxels receive detailed processing with feature extraction and occupancy prediction, while unoccupied voxels are handled more simply. This local quality approach reduces overall computational cost by avoiding unnecessary processing in empty regions while maintaining high processing capability where needed.
2Quantity of substance
If high compression ratios are achieved through aggressive compression, then storage requirements are reduced, but processing efficiency decreases
Solution Approach 1:
The patent performs preliminary processing by extracting features and generating occupancy predictions at coarser LoD levels before final decoding. The occupancy probability maps and feature representations are pre-computed and stored in compressed form, allowing rapid reconstruction during decoding without requiring intensive processing at the final stage, thus maintaining both compression ratio and processing efficiency.
3Loss of information
If detailed feature processing is performed at all LoD levels, then information completeness is maintained, but computational complexity increases
Solution Approach 1:
The patent applies partial processing by performing detailed feature extraction and occupancy prediction only on occupied voxels at each LoD level, rather than processing all voxels uniformly. The neural network models are applied selectively to regions containing data, maintaining information completeness for occupied regions while avoiding unnecessary computational complexity in unoccupied regions.
4Ease of operation
If consumer devices with limited computational power are used, then accessibility is improved, but processing capability deteriorates
Solution Approach 1:
The patent segments the point cloud processing into hierarchical LoD levels, allowing consumer devices to process only the necessary levels based on available computational resources. The octree structure enables progressive refinement from coarse to fine levels, so devices with limited power can achieve functional results at coarser levels while more powerful devices can process finer details, improving accessibility without sacrificing processing capability for capable devices.
Data Source
AI summary
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.