LiDAR Point Cloud Coding Using Pointwise Prediction
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Solution Overview
Problem
Conventional point cloud compression technologies face challenges in achieving high-speed acquisition, compression, and transmission of LiDAR data, and there is a need to improve coding efficiency by reducing redundancy in space and time.
Innovation Solution
A method and device for LiDAR point cloud coding that predicts the current point using a previously decoded point cloud, involving the generation of a prediction candidate list, determining a predicted point, calculating a residual point, quantizing it, and encoding it into a bitstream, along with the necessary indices and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud compression technology is used, then encoding and decoding can be performed on a point-by-point basis, but coding efficiency is insufficient and redundancy in space and time is not eliminated
Solution Approach 1:
The patent applies preliminary action by constructing a prediction candidate list from previously decoded points before encoding the current point. This allows the encoder to predict the current point's position using historical data, eliminating temporal redundancy and improving coding efficiency without losing essential information
Solution Approach 2:
The patent uses copying by creating a prediction candidate list that replicates previously decoded points. These copied points serve as predictors for current points, allowing the system to represent redundant information more compactly while maintaining the ability to reconstruct the original point cloud
2Speed
If high-speed acquisition and transmission of LiDAR point clouds is achieved, then real-time processing is enabled, but coding efficiency needs improvement to handle the data volume
Solution Approach 1:
The patent performs preliminary construction of prediction candidate lists using previously decoded points before the actual encoding of current points. This preliminary action enables fast prediction operations that maintain high acquisition speed while improving coding efficiency through intelligent redundancy elimination
Solution Approach 2:
The patent segments the point cloud processing into distinct stages: constructing prediction candidate lists from historical points, selecting predictors from these lists, and encoding residuals. This segmentation allows parallel processing and optimization at each stage, maintaining high speed while improving overall coding efficiency
Data Source
AI summary
A LiDAR point cloud coding method and a device use pointwise prediction. The point cloud coding method and the device predict a current point to be encoded/decoded by using a previously decoded point cloud to improve the coding efficiency of LiDAR point cloud coding.