Point Cloud Bitstream Coding for Low-Latency VR and AR
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services due to latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data by transmitting a bitstream that includes geometry and attribute information, using geometry-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC) coding, and incorporating feedback information to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is represented with high detail for VR/AR/MR services, then service quality is improved, but data processing complexity increases
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch is further segmented into foreground and background portions. This segmentation allows independent processing of different regions with different complexity requirements, reducing overall processing complexity while maintaining service quality.
Solution Approach 2:
The foreground portion containing important visual information is extracted and processed with higher detail, while the background portion is processed with lower detail. This extraction approach maintains service quality for critical regions while reducing processing complexity for less important regions.
2Loss of time
If more point data is processed to reduce latency, then real-time performance is improved, but encoding/decoding complexity increases
Solution Approach 1:
Different processing qualities are applied to different portions of the point cloud data. The foreground portion receives high-quality processing with more points and detailed encoding, while the background portion receives lower-quality processing with fewer points and simplified encoding. This reduces overall encoding/decoding complexity while maintaining real-time performance for critical foreground elements.
Solution Approach 2:
Instead of processing all point data with equal detail, the system applies partial processing focused on the foreground portion that requires higher quality. This selective processing approach reduces the total computational burden for encoding and decoding while maintaining real-time performance for the most important visual elements.
Data Source
AI summary
In a method for processing point cloud data according to embodiments, point cloud data can be encoded and transmitted to a bitstream. In a method for processing point cloud data according to embodiments, a bitstream comprising point cloud data can be received, and the point cloud data can be decoded.