Point Cloud Data Transmission Using Video-Based Compression
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Solution Overview
Problem
Current methods for transmitting and receiving point cloud data face challenges in efficiency, latency, and encoding/decoding complexity, particularly in providing high-quality point cloud services for applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method involving encoding and decoding point cloud data using a system that includes a point cloud video acquirer, encoder, file/segment encapsulator, and transmitter for transmission, and a receiver, decoder, and renderer for reception, employing video-based point cloud compression techniques to optimize data processing and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality for VR/AR/self-driving services, then service quality is improved, but data transmission complexity and latency increase
Solution Approach 1:
The patent segments point cloud data into multiple video sequences (geometry video, attribute videos, occupancy map video) that can be processed and transmitted independently. This segmentation allows parallel processing, reducing overall encoding/decoding complexity while maintaining high service quality for VR/AR/self-driving applications.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D video sequences through projection techniques. By mapping three-dimensional point cloud information onto two-dimensional video frames, the system leverages成熟的 video coding standards (H.264, H.265, VVC) to reduce encoding/decoding complexity while preserving the essential spatial and attribute information needed for high-quality services.
2Reliability
If point cloud data is transmitted with high quality for VR/AR/self-driving services, then service quality is improved, but transmission latency increases
Solution Approach 1:
By dividing point cloud data into separate video sequences (geometry, attributes, occupancy), the system can encode and transmit different segments in parallel or prioritize critical segments, reducing overall transmission latency while maintaining high service quality.
Solution Approach 2:
The patent performs preliminary transformation of 3D point cloud data into 2D video sequences before transmission. This pre-processing step enables the use of efficient video coding standards and allows for optimized transmission strategies, reducing latency during actual service delivery.
3Ease of manufacture
If traditional point cloud compression methods are used, then implementation is simpler, but encoding/decoding complexity and latency are higher
Solution Approach 1:
The patent replaces traditional mechanical point cloud processing methods with video-based compression techniques. By substituting specialized point cloud algorithms with standard video coding mechanisms (H.264, H.265, VVC), the system achieves lower encoding/decoding complexity and better performance while maintaining implementation feasibility.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving point cloud data; decoding the point cloud data; and rendering the point cloud data.