Dynamic Point Cloud Coding With Unified Motion and Octree Compression
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Solution Overview
Problem
Existing learning-based dynamic point cloud compression techniques are limited to either lossless or lossy coding, failing to efficiently handle dynamic point clouds with real-time processing needs in applications like autonomous driving and AR/VR.
Innovation Solution
A unified framework that integrates motion estimation and compensation with lossy and lossless octree coding, using finer-level features and reference point clouds to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If learning-based dynamic point cloud compression techniques are used, then compression efficiency is improved, but processing speed deteriorates
Solution Approach 1:
The compression framework is divided into separate functional modules: motion estimation module, motion compensation module, occupancy prediction module, and attribute prediction module. Each module processes specific aspects of the point cloud data independently, allowing for optimized processing pipelines that balance compression efficiency with processing speed through parallel computation and selective processing.
Solution Approach 2:
The system performs motion estimation and generates motion vectors in advance before the actual compression process. Reference point cloud frames are pre-processed and stored, enabling faster real-time compression by reusing pre-computed motion information and avoiding redundant calculations during the main compression workflow.
2Loss of information
If motion estimation and compensation are integrated with octree coding, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent merges motion estimation, motion compensation, and octree-based occupancy coding into a unified compression framework. The motion-compensated reference frames are directly integrated with the occupancy prediction process, allowing the system to exploit temporal redundancy through motion compensation while simultaneously exploiting spatial redundancy through octree coding, achieving synergistic compression efficiency without requiring separate independent systems.
Solution Approach 2:
The compression apparatus is designed with multi-functional modules that can adapt to different coding modes. The motion compensation module can operate in conjunction with octree coding or independently, and the occupancy prediction module can utilize either motion-compensated references or direct spatial relationships, providing flexibility that reduces overall system complexity by avoiding dedicated hardware for each specific function.
3Measurement precision
If finer-level features are used for prediction, then processing precision is improved, but computational complexity increases
Solution Approach 1:
The occupancy prediction process utilizes finer-level features from child voxels to predict occupancy status at specific local regions of the point cloud. By applying detailed feature analysis only where needed (in occupied regions) rather than uniformly across the entire point cloud, the system achieves high processing precision for occupancy detection while reducing overall computational complexity through selective application of intensive processing operations.
Data Source
AI summary
Some embodiments of a method may include: decoding a motion feature by accessing a motion bitstream; predicting a predicted feature based on the motion feature and one or more reference point cloud frames; decoding a first feature representing an occupancy status of a child level voxel; predicting a second feature based on the first feature and the predicted feature; and decoding a tree voxel occupancy status of the child level voxel via the second feature.