Point Cloud Bitstream Layering for Low-Latency Transmission
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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 high latency and encoding/decoding complexity.
Innovation Solution
A method for encoding and transmitting point cloud data through bitstreams, utilizing geometry-based and video-based point cloud compression coding, along with feedback information to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality for VR and self-driving services, then service quality is improved, but data transmission latency increases
Solution Approach 1:
The point cloud data is divided into multiple layers including base layer and enhancement layers. The base layer contains essential data for basic functionality, while enhancement layers provide additional quality. This segmentation allows progressive transmission where critical data is sent first to reduce latency, followed by optional enhancement data.
Solution Approach 2:
The patent employs variable bitrate encoding where the compression parameters are dynamically adjusted based on network conditions and service requirements. For time-sensitive applications like self-driving, higher bitrate is used to maintain quality, while for less critical applications, lower bitrate reduces transmission time.
2Manufacturing precision
If detailed point cloud data is processed to ensure high quality, then manufacturing precision is improved, but processing complexity increases
Solution Approach 1:
The point cloud data is organized into hierarchical structures with base layers and enhancement layers. This segmentation allows the decoder to process only the essential base layer data when complexity is a concern, while optionally processing enhancement layers when higher precision is required, thus providing a trade-off mechanism.
Solution Approach 2:
The patent enables partial processing where the decoder can choose to process only the base layer data for basic applications, or process all layers including enhancement layers for high-precision applications. This partial action approach reduces processing complexity for applications that don't require full detail.
3Adaptability or versatility
If complete point cloud data is transmitted for all services, then adaptability is improved, but data volume increases
Solution Approach 1:
The point cloud data is segmented into service-specific subsets and common base data. Each service (VR, self-driving, etc.) receives only the data subsets relevant to its requirements, while sharing common base layers. This segmentation reduces the total data volume transmitted while maintaining adaptability to different service needs.
Solution Approach 2:
The patent creates a universal base layer that serves multiple services simultaneously, and service-specific enhancement layers that are added only when needed. This multi-functionality approach allows the same base data to support various services, reducing redundant data transmission while maintaining service-specific adaptability.
Data Source
AI summary
A point cloud data transmission method according to an embodiment may comprise the steps of: encoding point cloud data; and transmitting the point cloud data. A point cloud data reception method according to an embodiment may comprise the steps of: receiving point cloud data; and decoding the point cloud data.