Point Cloud Coordinate Revision Using Capturing Laser Angles
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Solution Overview
Problem
Conventional point cloud coding techniques, particularly in Geometry-based Point Cloud Compression (G-PCC), suffer from inefficiencies in geometry coding that lead to distortion of x, y, and z coordinates, despite having potential for utilizing laser capture information to reduce this distortion.
Innovation Solution
The proposed method involves determining the capturing laser for each point in the point cloud and revising its coordinates based on the laser's elevation and azimuthal angles to improve coding efficiency and reduce geometric distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud coding techniques are used, then the coding process is simple, but geometry coding efficiency is poor and coordinate distortion occurs
Solution Approach 1:
The patent applies preliminary action by revising the coordinates of points in the point cloud before encoding. Specifically, the z-coordinate of each point is revised based on the elevation angle of the capturing laser, and the x and y coordinates are revised based on the azimuthal angle. This pre-processing step prepares the data in advance to improve coding efficiency while maintaining manageable process complexity through structured coordinate revision operations.
2Measurement precision
If laser capture information is utilized to revise coordinates, then geometric distortion is reduced, but processing complexity increases
Solution Approach 1:
The patent applies parameter changes by modifying the coordinate values of points in the point cloud based on laser capture parameters. The z-coordinate is revised using the elevation angle parameter, and the x and y coordinates are revised using the azimuthal angle parameter. This systematic parameter transformation approach improves coordinate precision while managing processing complexity through well-defined mathematical operations.
3Productivity
If coordinate revision based on laser information is implemented, then coding performance improves, but computational requirements increase
Solution Approach 1:
The patent applies local quality by selectively revising coordinates based on the specific characteristics of each point and its corresponding laser capture parameters. Each point in the point cloud is processed individually with its specific elevation and azimuthal angle data, allowing for optimized coordinate revision that improves compression performance while managing computational energy through targeted processing rather than uniform treatment of all points.
Data Source
AI summary
Embodiments of the present disclosure provide a method for point cloud coding. In the method, for a conversion between a current coding unit of a point cloud sequence and a bitstream of the point cloud sequence, whether at least one condition associated with at least one coordinate of a point in the current coding unit is satisfied is determined. In accordance with a determination that the at least one condition is satisfied, the at least one coordinate is updated based on a capturing laser capturing the point. The conversion is performed based on the at least one updated coordinate of the point.