Point Cloud Occupancy Prediction for Context-Adaptive Compression
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Solution Overview
Problem
Current point cloud compression methods are inefficient in encoding and decoding three-dimensional data, particularly due to the sparse population of point clouds, which complicates the coding of geometry and results in high storage and bandwidth requirements.
Innovation Solution
The method involves context-adaptively coding occupancy data for child sub-volumes based on occupancy scores calculated from neighboring sub-volumes, using weights that reflect the correlation between neighboring and child sub-volume occupancies, and employing entropy encoding and decoding techniques to generate and reconstruct compressed bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional point cloud compression methods are used, then encoding and decoding can be performed, but compression efficiency is poor and storage/bandwidth requirements are high
Solution Approach 1:
The patent performs preliminary occupancy prediction for child sub-volumes using occupancy scores calculated from neighboring sub-volumes before actual encoding. This preliminary action allows the encoder to prepare context information in advance, selecting the most appropriate context models based on predicted occupancy patterns, thereby improving compression efficiency without increasing storage requirements
Solution Approach 2:
The patent dynamically changes encoding parameters by selecting different context models based on occupancy scores. The occupancy score itself is a derived parameter that combines occupancy information from multiple neighboring sub-volumes with weights reflecting their correlation. This parameter-driven approach allows adaptive adjustment of encoding strategies to match the actual data characteristics, improving compression performance
2Productivity
If occupancy data is coded without considering spatial redundancy, then encoding is simpler, but compression efficiency deteriorates
Solution Approach 1:
The patent performs preliminary occupancy prediction using neighboring sub-volume information before actual encoding. The occupancy score is calculated in advance by combining occupancy status of neighboring sub-volumes with correlation weights, allowing the encoder to prepare context selection decisions before processing each child sub-volume, thus improving compression efficiency while managing complexity through structured pre-computation
Solution Approach 2:
The patent uses occupancy information from neighboring sub-volumes as a copy or proxy to predict occupancy of child sub-volumes. By copying the occupancy pattern information from spatially adjacent regions and weighting it by correlation, the method exploits spatial redundancy without requiring direct copying of actual point data, thereby improving compression while maintaining reasonable encoding complexity
3Productivity
If neighbourhood-based occupancy prediction is used, then compression efficiency improves, but encoding complexity increases
Solution Approach 1:
The patent manages encoding complexity by changing parameters in a structured way: occupancy scores are calculated using a defined formula combining neighboring occupancy information with pre-determined weights. The context selection process uses these scores to select from a有限的 set of context models. This parameter-driven approach with predefined weight structures and context model sets improves compression efficiency while controlling encoding complexity through systematic parameter management rather than arbitrary complexity increases
Data Source
AI summary
Methods and devices for encoding a point cloud, where occupancy data for child sub-volumes is context-adaptively encoded based on contexts selected, at least in part, using an occupancy score determined for each child sub-volume. The occupancy score for each child sub-volume of a parent sub-volume is determined based on the occupancy status of a plurality of neighbouring sub-volumes neighbouring the parent sub-volume and a respective weight assigned to each of the neighbouring sub-volumes vis-à-vis that child sub-volume, and wherein the respective weight reflects a strength of correlation between occupancy of that neighbouring sub-volume and occupancy of that child sub-volume.