Point Cloud Bitstream Encoding for Low-Latency Processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for services like virtual reality, augmented reality, and self-driving, due to latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data, including geometry and attribute information, using bitstreams to facilitate efficient processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is represented using tens of thousands to hundreds of thousands of point data, then the quality of point cloud content is improved, but the processing complexity and latency increase
Solution Approach 1:
The patent divides the point cloud data processing into separate encoding and decoding stages, with the encoder compressing the data into a compact bitstream representation and the decoder reconstructing it. This segmentation allows high-quality point cloud representation while reducing processing complexity through efficient compression algorithms.
Solution Approach 2:
The patent creates a compressed bitstream copy of the original point cloud data that preserves essential geometric and attribute information. This bitstream copy enables efficient transmission and storage while maintaining the ability to reconstruct high-quality point cloud content when needed.
2Measurement precision
If point cloud data is represented using tens of thousands to hundreds of thousands of point data, then the quality of point cloud content is improved, but the latency increases
Solution Approach 1:
The patent performs encoding of point cloud data into a compressed bitstream format in advance, before transmission or storage. This preliminary compression action reduces the amount of data that needs to be processed later, thereby reducing latency during playback or rendering while maintaining quality.
3Productivity
If point cloud data is encoded and decoded, then the transmission efficiency is improved, but the encoding/decoding complexity increases
Solution Approach 1:
The patent transforms point cloud data from its original high-dimensional format into a compressed bitstream representation by changing key parameters such as geometric precision and attribute sampling rates. This parameter transformation enables efficient transmission while managing encoding/decoding complexity through controlled compression.
Data Source
AI summary
A method for transmitting point cloud data according to embodiments may encode and transmit point cloud data. A method for receiving point cloud data according to embodiments may receive and decode point cloud data.