Point Cloud Attribute Coding With Bottom-Up Probability Estimation
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Solution Overview
Problem
Existing learning-based methods for octree-based attribute coding in point clouds rely solely on parent level information for probability estimation, leading to inaccurate probability distribution and high complexity, which affects the efficiency of arithmetic coding.
Innovation Solution
A bottom-up approach that utilizes finer level details for feature extraction and aggregation, followed by encoding attribute information, and employs hyperprior encoding for enhanced probability estimation and arithmetic coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parent level information is used for probability estimation in octree-based attribute coding, then the method is simple to implement, but the probability distribution accuracy is poor
Solution Approach 1:
The patent inverts the traditional top-down probability estimation approach by implementing a bottom-up method. Instead of estimating probabilities from parent level information, the system aggregates attribute information from child voxels to estimate probabilities at the current level, thereby improving accuracy without proportionally increasing complexity
Solution Approach 2:
The patent introduces a new dimension of information aggregation by combining attributes from multiple child voxels across different spatial positions. This multi-dimensional aggregation approach enriches the probability estimation basis, improving accuracy while distributing the computational load across multiple processing stages
2Measurement precision
If finer level details are used for feature extraction, then the probability estimation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the point cloud data into an octree structure with multiple hierarchical levels. By processing and aggregating information from child voxels at finer levels before estimating probabilities at coarser levels, the system improves accuracy while managing computational complexity through hierarchical decomposition
Solution Approach 2:
The patent performs preliminary feature extraction and aggregation from finer level details before the main probability estimation process. This preliminary action prepares refined attribute information that improves subsequent probability estimation accuracy while organizing computations in an efficient sequence
3Loss of information
If traditional arithmetic coding is used with parent level information, then the bit rate is higher, but the coding process is simpler
Solution Approach 1:
The patent implements a feedback mechanism where the aggregated attribute information from child voxels is used to refine probability estimates. This feedback loop enables more accurate probability distributions that reduce the bit rate required for arithmetic coding, while the structured feedback process manages complexity through systematic information reuse
Data Source
AI summary
In one implementation, a method of decoding point cloud data is presented, comprising: decoding features representing voxel attributes in an octree structure, wherein a decoded feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed; determining an attribute probability of the current voxel based on the decoded feature for the current voxel; decoding attribute information of voxels in the octree structure, wherein the attribute information for the current voxel is decoded based on the attribute probability for the current voxel; and reconstructing the point cloud based on the attribute information of voxels in the octree structure. On the encoder side, the features are extracted and encoded into the bitstream.