Point Cloud Data Transmission Using Patch Segmentation
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data, leading to latency and encoding/decoding complexity in applications such as virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data using bitstreams, incorporating geometry-based and video-based compression techniques, and utilizing 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 and processing complexity increase
Solution Approach 1:
The point cloud data is divided into multiple patches or tiles, allowing parallel processing and selective transmission of only relevant portions. This segmentation enables the system to maintain high service quality for visible regions while reducing overall data transmission latency by excluding invisible or less important areas.
Solution Approach 2:
The system performs preliminary encoding and preparation of point cloud data into multiple patches before transmission. By pre-processing the data into organized segments with associated metadata, the system reduces real-time processing complexity and enables faster decoding at the receiver端, thereby reducing transmission latency while maintaining service quality.
2Reliability
If point cloud data is transmitted with high quality for VR and self-driving services, then service quality is improved, but encoding and decoding complexity increase
Solution Approach 1:
By dividing point cloud data into multiple patches with hierarchical organization, the encoding and decoding processes can be distributed and parallelized. Each patch can be processed independently, reducing the computational complexity burden on single processing units while maintaining overall high service quality through comprehensive coverage.
Solution Approach 2:
The system applies partial processing to patches that are not currently visible or less important, reducing overall processing complexity. Meanwhile, full high-quality processing is applied only to visible and critical regions, achieving high service quality where needed while minimizing unnecessary computational complexity in other areas.
3Loss of information
If all point cloud data is transmitted, then complete information is provided, but transmission bandwidth and processing load increase
Solution Approach 1:
The point cloud data is segmented into multiple patches, allowing the system to selectively transmit only those patches that are currently visible or relevant to the user's viewpoint. This segmentation enables the system to maintain information completeness for visible regions while significantly reducing overall data volume by excluding invisible or less important areas.
Solution Approach 2:
The system extracts and transmits only the essential and visible portions of point cloud data based on viewpoint information and importance metrics. By taking out only the necessary patches and their associated metadata, the system maintains sufficient information completeness for high-quality service while reducing total data transmission volume.
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 including point cloud data; and decoding the point cloud data.