Point Cloud Data Transmission Using LOD-Based Neighbor Selection
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently transmitting and receiving large amounts of data, particularly in reducing latency and improving compression performance, especially in applications like virtual reality and self-driving services.
Innovation Solution
A method and apparatus for efficiently encoding and decoding point cloud data by selecting neighbor points based on levels of detail (LODs) and calculating the maximum neighbor point distance, which reduces the size of the attribute bitstream and enhances compression efficiency through geometry-based point cloud compression (G-PCC).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If all neighbor points are selected for attribute encoding, then compression efficiency is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of neighbor points based on their individual contributions to attribute prediction accuracy. Instead of uniformly processing all neighbor points, the system evaluates each point's attribute correlation with the target point and selectively includes only those that provide meaningful prediction value. This selective approach reduces the number of points requiring processing while maintaining compression efficiency, thereby resolving the contradiction between compression performance and processing complexity.
2Loss of substance
If maximum neighbor point distance is increased to capture more points, then compression efficiency improves, but latency increases
Solution Approach 1:
The patent implements partial action by determining an optimal subset of neighbor points within the maximum distance range that provides sufficient prediction accuracy without requiring processing of all possible points. The system identifies and processes only the necessary portion of neighbor points that contribute meaningfully to attribute prediction, avoiding the excessive processing time that would result from evaluating all points within the maximum distance. This selective processing reduces latency while maintaining compression efficiency.
3Loss of substance
If attribute correlation is considered for selecting neighbor points, then compression efficiency improves, but encoding complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing attribute correlation information for potential neighbor points before the actual encoding process. This pre-computation allows the encoder to quickly identify and select appropriate neighbor points based on pre-established correlation metrics, avoiding the need to perform complex correlation calculations during real-time encoding. The preliminary preparation reduces encoding complexity while maintaining the ability to exploit attribute correlations for improved compression efficiency.
Data Source
AI summary
Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points of the point cloud data, generating one or more LODs based on the geometry information and selecting one or more neighbor points of each point to be attribute-encoded based on the one or more LODs, wherein the selected one or more neighbor points of each point are located within a maximum neighbor point distance, encoding attribute information of each point based on the selected one or more neighbor points of each point, and transmitting the encoded geometry information, the encoded attribute information, and signaling information.