3D Point Cloud Compression With Octree Entropy and Block Parallelism
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Solution Overview
Problem
The processing of large-scale 3D point cloud data is computationally expensive and inefficient for consumer devices due to the large number of points required, leading to high computational costs and significant network traffic consumption.
Innovation Solution
A learned deep entropy model over a tree structure, such as an octree, is used for lossless compression of point cloud data, utilizing local and global topology information, and incorporating a block-based compression scheme for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud processing methods are used, then processing accuracy is maintained, but computational cost and processing time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple blocks or regions, allowing parallel processing of different segments. This segmentation enables the system to process large-scale point clouds more efficiently by distributing computational load across multiple processing units, thereby improving processing speed while managing computational resources better.
Solution Approach 2:
The patent transforms the processing approach by introducing hierarchical levels or multi-resolution representations of point cloud data. Instead of processing all points at full resolution simultaneously, the system processes data at different dimensional scales, reducing the overall computational burden while maintaining processing accuracy where needed.
2Measurement precision
If high-resolution point cloud data is processed, then processing precision is improved, but network traffic consumption increases
Solution Approach 1:
The system extracts only the essential or salient features from high-resolution point cloud data for transmission and processing. By identifying and transmitting only the most important geometric features or changed regions rather than complete high-resolution data, the system maintains processing precision while significantly reducing network traffic consumption.
Solution Approach 2:
The patent implements selective processing where only portions of the point cloud data requiring high precision are processed at full resolution, while other regions are processed at lower resolutions. This partial action approach ensures processing precision is maintained where necessary while reducing overall network traffic and computational requirements.
Data Source
AI summary
A method, apparatus or system for processing point cloud information can involve a learned deep entropy model over octrees for lossless compression/decompression of 3D point cloud data, wherein self-supervised compression/decompression involves an adaptive entropy coder operating on a tree-structured conditional entropy model and utilizing information from the local neighborhood as well as the global topology from the tree structure.