Point Cloud Compression Layers for Low-Latency 3D Transmission
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for applications like virtual reality, augmented reality, and self-driving services due to high latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, along with feedback information to optimize data processing based on user interaction, reducing latency and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high fidelity for VR and self-driving services, then service quality is improved, but data transmission bandwidth and processing complexity increase significantly
Solution Approach 1:
The point cloud data is segmented into multiple layers including base layer and enhancement layers, allowing progressive transmission and processing. Each layer contains specific point cloud attributes or resolution levels, enabling flexible quality adjustment without processing the entire high-fidelity dataset simultaneously.
Solution Approach 2:
The patent transforms point cloud data parameters by converting 3D coordinates into 2D projections and adjusting point density parameters based on distance from the camera. This parameter transformation reduces data complexity while maintaining perceptual quality for VR and self-driving applications.
2Productivity
If point cloud data is compressed to reduce transmission bandwidth, then transmission efficiency is improved, but decoding complexity and latency increase
Solution Approach 1:
Point cloud data is pre-processed and organized into hierarchical structures before transmission. The base layer and enhancement layers are prepared in advance, allowing the receiver to quickly decode the essential base layer first and progressively add enhancement layers, reducing overall latency compared to decompressing a single large compressed file.
Solution Approach 2:
The patent introduces an intermediate representation format that serves as a mediator between the raw point cloud data and the final decoded output. This intermediate format maintains sufficient structure for efficient compression while enabling faster decoding through simplified transformation operations.
3Manufacturing precision
If high-resolution point cloud data is processed to maintain quality for VR services, then visual quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies different quality levels to different regions of the point cloud data based on their importance. Regions closer to the camera or containing critical objects for self-driving services are processed with higher precision, while distant or less important regions use lower precision, maintaining overall visual quality while reducing total processing time.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.