Point Cloud Encoding via Spatial Grid and Projection Planes
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Solution Overview
Problem
The efficiency of point cloud data encoding is hindered by the dispersed position distribution of sparse point cloud data, leading to long encoding waiting times and increased bandwidth usage, which negatively impacts user experience.
Innovation Solution
A method that involves determining a space grid structure and filling order for point cloud points, using residual information to encode initial point cloud data and decode point cloud data bitstreams, thereby reducing encoding complexity and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional encoding methods are used on sparse point cloud data with dispersed position distribution, then encoding accuracy can be maintained, but encoding time increases significantly and efficiency decreases
Solution Approach 1:
The patent divides the three-dimensional space into multiple two-dimensional projection planes (front view, side view, top view) and further segments points into foreground and background layers. This segmentation allows independent processing of different point subsets, reducing the computational complexity of encoding dispersed point cloud data while maintaining spatial relationships through projection coordinates.
Solution Approach 2:
The patent introduces projection planes as an intermediary structure to represent three-dimensional point cloud data. By projecting 3D points onto 2D planes and using projection coordinates as intermediate representations, the encoding process can efficiently handle dispersed points without directly processing their full 3D spatial relationships, thus reducing encoding time.
2Measurement precision
If more discrete points are used to accurately reflect spatial information, then representation accuracy improves, but data volume and bandwidth requirements increase
Solution Approach 1:
The patent extracts and separates different attributes of point cloud data into distinct components: spatial position (represented by projection coordinates on 2D planes), depth information (separate foreground and background layers), and attribute information (color, reflectivity). This extraction allows selective compression and efficient representation, reducing overall data volume while preserving essential spatial accuracy.
Solution Approach 2:
The patent transforms three-dimensional point cloud data into two-dimensional projection representations. By projecting 3D points onto 2D planes and using (u, v) projection coordinates combined with depth values, the method reduces the dimensional complexity of spatial representation, thereby reducing data volume while maintaining the ability to accurately reconstruct 3D spatial information.
Data Source
AI summary
A point cloud data encoding method is provided, which includes: acquiring initial point cloud data in a point cloud data processing environment; determining a space grid structure corresponding to the initial point cloud data; determining a filling order of different point cloud points in the initial point cloud data in the space grid structure; determining, based on the filling order of different point cloud points in the initial point cloud data in the space grid structure, residual information matched with the initial point cloud data; and encoding, according to the residual information, the initial point cloud data to obtain target point cloud data. A point cloud data decoding method, a point cloud data processing method, apparatuses, an electronic device, a computer program product, and a computer-readable storage medium are also provided.