Point Cloud Compression for Moving Bodies Using Traffic-Based Region Segmentation
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Solution Overview
Problem
Existing techniques for compressing point cloud data are not suitable for the travel of a moving body, such as an autonomous driving vehicle.
Innovation Solution
An information processing device that obtains point cloud data and traffic information, determines specific areas based on this information, and executes different compression controls for point cloud data associated with these areas, ensuring optimal compression for the moving body's travel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform compression control is applied to all point cloud data, then compression efficiency is improved, but processing precision in critical areas deteriorates
Solution Approach 1:
The patent applies different compression control parameters to different spatial regions of the point cloud data. Specifically, it divides the space into multiple regions and assigns different compression rates or processing methods to each region based on its importance to the moving body's operation, thereby maintaining high precision in critical areas while achieving overall compression efficiency.
Solution Approach 2:
The patent segments the point cloud data into multiple regions based on spatial coordinates and importance levels. By dividing the data into distinct segments (e.g., high-importance regions near the moving body, medium-importance regions, and low-importance distant regions), it enables differentiated compression strategies for each segment, resolving the contradiction between uniform compression and localized precision requirements.
2Quantity of substance
If high compression rate is applied to reduce data size, then transmission efficiency is improved, but information loss increases
Solution Approach 1:
The patent applies varying compression rates to different regions of the point cloud data based on their importance. Critical regions close to the moving body use lower compression rates to preserve essential information, while less critical distant regions use higher compression rates to reduce data size, thereby optimizing the balance between data size reduction and information preservation.
3Manufacturing precision
If detailed point cloud data is processed, then processing accuracy is improved, but processing time increases
Solution Approach 1:
The patent prioritizes processing accuracy for point cloud data in critical regions (e.g., areas close to the moving body or containing important objects) while applying simplified processing to less critical regions. This regional differentiation maintains high processing accuracy where needed while reducing overall processing time through selective optimization.
Data Source
AI summary
An information processing device includes a point cloud data obtainer that obtains point cloud data obtained by sensing a vicinity of a moving body or stored in a point cloud database; a traffic information obtainer that obtains traffic information in the vicinity of the moving body; a determiner that determines a specific area included in the vicinity of the moving body based on the traffic information; a compressor that executes compression control on first point cloud data and second point cloud data differently from each other, the first point cloud data and the second point cloud data being included in the point cloud data, the first point cloud data being associated with the specific area, the second point cloud data being associated with an area other than the specific area; and an outputter that outputs compressed data obtained by executing the compression control on the point cloud data.