Point Cloud Direct Coding for Faster Tree-Based Compression
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Solution Overview
Problem
Current point cloud compression methods, particularly those using tree-based structures, face inefficiencies in dealing with large datasets and isolated points, leading to high computational burden and bandwidth/memory costs, as they do not effectively utilize direct coding for positional information.
Innovation Solution
The proposed solution involves a method for encoding and decoding point clouds using direct coding mode (DCM), where eligibility for DCM is determined based on occupancy data from neighboring or parent nodes, allowing for direct encoding of positional coordinates in eligible sub-volumes, reducing the need for recursive splitting and entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If tree-based compression methods are used for point clouds, then compression is achieved, but coding complexity and time increase significantly
Solution Approach 1:
The method segments the point cloud data by dividing the 3D space into a grid structure with multiple cells. Each cell is processed independently to determine if it contains points, creating a hierarchical representation that reduces overall coding complexity while maintaining compression effectiveness.
Solution Approach 2:
The method extracts and processes only the necessary information from each grid cell - specifically, whether the cell is occupied by points and the coordinates of those points. This selective extraction avoids processing unnecessary data and reduces coding complexity.
2Loss of substance
If tree-based compression methods are used for point clouds, then compression is achieved, but coding time increases significantly
Solution Approach 1:
The method performs preliminary organization of point cloud data into a grid structure before compression. By pre-processing the data to identify occupied cells and their coordinates, the actual compression process becomes faster and more efficient, reducing overall coding time.
3Loss of time
If direct coding is applied to all points, then coding time is reduced, but bandwidth and memory usage increase
Solution Approach 1:
The method applies different coding strategies to different regions of the point cloud based on local characteristics. Occupied grid cells are processed with direct coding to reduce time, while empty cells are handled more efficiently. This localized approach optimizes both coding time and resource usage.
4Measurement precision
If recursive splitting is performed for all sub-volumes, then compression accuracy is improved, but computational burden increases
Solution Approach 1:
The method applies recursive splitting only to occupied grid cells that contain points, rather than performing splitting on all sub-volumes uniformly. This partial application of the splitting operation maintains compression accuracy for regions that need it while avoiding unnecessary computational burden in empty regions.
Data Source
AI summary
Methods and devices for coding point clouds using direct coding mode to code coordinates of a point within a sub-volume associated with a current node instead of a pattern of occupancy for child nodes. Eligibility for use of direct coding is based on occupancy data from another node. If eligible, then a flag is represented in the bitstream to signal whether direct coding is applied to points in the sub-volume or not.