Point Cloud Attribute Coding for Duplicate Point Prediction
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Solution Overview
Problem
Current point cloud coding methods are inefficient due to the high computational cost of finding nearest neighbors in 3D space and failure to utilize inter-component correlations among color channels, leading to performance losses in coding efficiency.
Innovation Solution
The method involves sorting duplicate points by geometry position and attribute values, predicting attribute values of subsequent points from the first point in each group, and using prediction residuals to reconstruct attribute values, thereby improving coding efficiency by leveraging inter-component correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud coding methods are used, then the coding process can be completed, but the computational cost is high due to finding nearest neighbors in 3D space
Solution Approach 1:
The patent segments duplicate points into groups based on geometry position and sorts them by attribute values. This segmentation allows the encoder to process points in ordered groups rather than searching all points, significantly reducing computational cost while maintaining coding efficiency
Solution Approach 2:
The patent performs preliminary sorting of duplicate points by geometry position and attribute values before coding. This preliminary organization enables more efficient subsequent processing by eliminating the need for expensive nearest neighbor searches during the actual coding phase
2Productivity
If conventional point cloud coding methods are used, then coding can be performed, but performance is lost due to failure to utilize inter-component correlations among color channels
Solution Approach 1:
The patent merges the coding of multiple color channels (RGB) by exploiting inter-component correlations. Instead of coding each channel independently, the method combines them and uses prediction residuals to reconstruct all channels, reducing performance loss and improving coding efficiency
Solution Approach 2:
The patent uses prediction residuals as feedback to reconstruct attribute values. The residuals represent the difference between actual and predicted values, and this feedback mechanism enables accurate reconstruction of color channels while leveraging inter-component correlations
Data Source
AI summary
A method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; reconstructing, based on the data, a first attribute value of a first duplicate point from among a plurality of duplicate points corresponding to a single geometry position; obtaining at least one prediction residual corresponding to at least one remaining attribute value of at least one remaining duplicate point from among the plurality of duplicate points; reconstructing the at least one remaining attribute value based on the reconstructed first attribute and the at least one prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first attribute value and the reconstructed at least one remaining attribute value.