Point Cloud Compression Pipeline for Low-Latency Streaming
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data due to latency and encoding/decoding complexity, which is crucial for applications like VR, AR, and self-driving services.
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 bitstreams, with multiplexing and decoding processes optimized for efficient 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 patent segments point cloud data into multiple layers including occupancy information, geometry information, and attribute information. This hierarchical segmentation allows progressive transmission where critical occupancy data is sent first for rapid scene establishment, followed by geometry and attribute data for detailed rendering, thereby reducing overall transmission latency while maintaining service quality
Solution Approach 2:
The patent extracts and prioritizes transmission of occupancy information separately from detailed geometry and attribute data. By taking out the most critical structural information first and transmitting it in an optimized format, the system achieves faster initial scene reconstruction with reduced latency, while detailed data follows to enhance service quality
2Productivity
If point cloud data is compressed to reduce transmission latency, then transmission efficiency is improved, but encoding/decoding complexity increases
Solution Approach 1:
The encoding process is segmented into distinct stages: occupancy encoding, geometry encoding, and attribute encoding. Each stage processes specific data types with optimized algorithms, avoiding the need for complex all-in-one compression. This modular segmentation improves transmission efficiency while keeping individual encoding/decoding modules manageable in complexity
Solution Approach 2:
The patent employs dynamic encoding strategies where the level of detail and compression applied to different data layers can be adjusted based on transmission conditions and service requirements. This dynamic approach optimizes transmission efficiency without requiring maximum complexity in all encoding scenarios
3Manufacturing precision
If detailed point cloud data is transmitted for high-quality rendering, then rendering quality is improved, but data transmission bandwidth requirements increase
Solution Approach 1:
The patent segments point cloud data into occupancy, geometry, and attribute layers that can be transmitted at different bandwidth allocations. This segmentation allows the system to allocate bandwidth prioritarily to structural occupancy data for scene establishment, then allocate remaining bandwidth to geometry and attribute data for rendering quality, optimizing the quality-bandwidth tradeoff
Solution Approach 2:
The patent applies different compression and transmission qualities to different parts of the point cloud data structure. Occupancy information is transmitted with high fidelity as it defines the overall scene structure, while geometry and attribute data can use progressive or selective transmission based on local importance, achieving good rendering quality with reduced overall bandwidth requirements
Data Source
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AI summary
A point cloud data transmission method according to the 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 the embodiments may comprise the steps of receiving a bitstream comprising point cloud data, and decoding the point cloud data.