Point Cloud Compression Using Significance Maps for Low Latency
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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, including geometry and attribute encoding, with features like octree geometry coding, voxelization, and scalable lifting transform coding, to optimize data transmission and rendering.
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 latency increases
Solution Approach 1:
The point cloud data is segmented into multiple layers including geometry information, attribute information, and significance maps. This segmentation allows selective transmission of critical data elements first, enabling progressive reconstruction and reducing overall transmission latency while maintaining service quality.
Solution Approach 2:
Significance maps are introduced as intermediary data structures that guide the reconstruction process. These maps indicate the importance of different point cloud regions, allowing the receiver to prioritize processing and rendering of significant areas, thereby reducing perceived latency without sacrificing overall quality.
2Reliability
If detailed point cloud data is processed to maintain high quality, then service quality is improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent applies different processing and compression strategies to different regions of the point cloud based on their significance. High-priority regions undergo more detailed processing while less critical regions use simplified methods, reducing overall encoding/decoding complexity while maintaining quality where it matters most.
Solution Approach 2:
The system dynamically adjusts encoding parameters such as quantization precision and transformation depth based on the significance map and available resources. This adaptive parameter adjustment reduces computational complexity in less critical areas while preserving quality in important regions.
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 including 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.