Point Cloud Encoding Using Global Occupancy Maps
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Solution Overview
Problem
The increasing scale of point cloud data collected by 3D sensors poses a challenge in effectively encoding and compressing this data, necessitating improved coding or compression efficiency.
Innovation Solution
A point cloud encoding method that involves obtaining global matched patches across frames, determining union patch occupancy maps, and packing these maps to create a global occupancy map, which is used to encode the point clouds, ensuring spatial consistency and reducing bitstream transmission overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud encoding methods are used, then encoding can be performed without complex processing, but coding efficiency is low and compression performance is poor
Solution Approach 1:
The point cloud data is divided into multiple patches, and each patch is processed independently to generate occupancy maps. This segmentation allows parallel processing and improves coding efficiency without requiring complex global processing of the entire point cloud
Solution Approach 2:
Occupancy maps from multiple patches across different frames are merged into a global occupancy map. This combining approach consolidates spatial information from multiple sources, improving compression performance while maintaining a systematic encoding structure
2Productivity
If spatial consistency is maintained across frames, then coding efficiency improves, but processing complexity increases
Solution Approach 1:
The global occupancy map is constructed in advance by merging occupancy maps from multiple frames before the actual encoding process. This preliminary action establishes spatial consistency across frames, enabling more efficient subsequent encoding without requiring complex real-time processing
Solution Approach 2:
The global occupancy map serves as a reference structure that is applied across multiple frames. By copying and adapting this reference structure to each frame, spatial consistency is maintained while avoiding the need for complex frame-by-frame analysis
Data Source
AI summary
A point cloud encoding method is provided to improve coding efficiency. The method includes: obtaining global matched patches in N frames of point clouds in a point cloud group; determining M union patch occupancy maps corresponding to M sets, where a union patch occupancy map corresponding to an mth set is a union set of occupancy maps of all global matched patches in the mth set; packing the M union patch occupancy maps to obtain a global occupancy map; packing each of the N frames of point clouds to obtain occupancy maps of the N frames of point clouds; and encoding the N frames of point clouds based on the occupancy maps of the N frames of point clouds.