Point Cloud Encoding Subgrouping by Occupancy Map Size
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Solution Overview
Problem
The increasing scale of point cloud data collected by 3D sensors poses a challenge in effectively encoding this data, necessitating improved encoding efficiency.
Innovation Solution
A point cloud encoding method that groups point clouds into subgroups based on feature information, specifically using the size of the occupancy map to determine subgrouping, which influences the size of depth and texture maps, thereby enhancing encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is encoded without subgrouping, then the encoding process is simpler, but the encoding efficiency is lower
Solution Approach 1:
The patent divides the point cloud data into multiple subgroups based on occupancy map size characteristics. This segmentation allows different encoding strategies to be applied to different subgroups, improving overall encoding efficiency while managing complexity through structured organization of the data processing workflow.
2Manufacturing precision
If the occupancy map size is increased to improve coding precision, then the depth map and texture map quality improve, but the memory requirements and processing complexity increase
Solution Approach 1:
The patent applies different occupancy map sizes to different subgroups of point cloud data based on their specific characteristics. Rather than using a uniform occupancy map size for all data, the system tailors the occupancy map dimensions to local requirements, thereby achieving high coding precision where needed while avoiding unnecessary complexity and resource consumption in other areas.
Data Source
AI summary
A point cloud encoding method includes grouping a to-be-coded point cloud group into a plurality of subgroups, where the grouping a to-be-coded point cloud group into a plurality of subgroups includes pre-grouping a plurality of frames of point clouds in the to-be-coded point cloud group to obtain a pre-grouped subgroup, and determining, based on feature information of the pre-grouped subgroup, that the pre-grouped subgroup is one of the plurality of subgroups, where the feature information represents a size of an occupancy map of a point cloud in the pre-grouped subgroup, and encoding a point cloud included in the plurality of subgroups.