TriSoup Centroid Coding With Motion-Compensated Neighborhood Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The large data size of point clouds necessitates efficient compression techniques for storage and transmission, with lossy compression impacting visual quality and lossless compression being crucial for certain applications like autonomous driving.
Innovation Solution
A point cloud coding system that encodes and decodes point cloud sequences using a context-based approach, reducing reconstruction error by determining a second centroid vertex from a first vertex, and employing a dynamic reduction function to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed to reduce data size, then storage and transmission efficiency is improved, but visual quality and reconstruction accuracy deteriorate
Solution Approach 1:
The patent segments the point cloud data into multiple octants (8 sub-regions) and further divides each octant into multiple blocks. This hierarchical segmentation allows independent processing and compression of different regions, enabling selective preservation of important geometric features while compressing less critical areas, thus balancing data size reduction with reconstruction accuracy.
Solution Approach 2:
The patent applies different compression strategies and quality levels to different regions of the point cloud. By analyzing the local characteristics of each block and octant, the system can preserve high fidelity in regions requiring accuracy (such as areas with important geometric features) while applying more aggressive compression in regions where visual quality is less critical, thereby maintaining overall reconstruction quality while reducing total data size.
2Manufacturing precision
If complex compression techniques are applied to maintain visual quality, then reconstruction accuracy is improved, but processing complexity and computational resources increase
Solution Approach 1:
The patent divides the point cloud into a hierarchical structure of octants and blocks, allowing complex processing to be applied locally to small regions rather than to the entire dataset. This segmentation reduces the computational burden at each processing stage while maintaining overall visual quality, as each small block can be processed independently with simpler algorithms.
Solution Approach 2:
The patent performs preliminary organization of point cloud data into octants and blocks before compression, and pre-calculates certain geometric properties and statistics for each region. This preliminary structuring simplifies subsequent compression operations by organizing data in a way that facilitates efficient processing, reducing the complexity of the main compression algorithm while preserving visual quality.
Data Source
AI summary
One or more methods, apparatuses, computer-readable storage mediums, and systems for entropy coding vertex information of an edge in a voxelized space of a point cloud are disclosed. Symbols of a neighborhood configuration of a current edge may be determined based on one or more already coded edges. The already coded edges may be selected from a spatial topology of edges. The use of a motion-compensated point cloud for coding centroid residual values may enhance interframe correlation used for determining a context or probability model. This increased correlation may improve the selection of coders, leading to enhanced compression of centroid residual values.