Point Cloud Data Transmission Encoding Latency Reduction
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Solution Overview
Problem
Existing technologies face challenges in efficiently transmitting and receiving point cloud data due to high latency and encoding/decoding complexity, which affects the quality of services like virtual reality, augmented reality, and self-driving applications.
Innovation Solution
A method and apparatus for efficiently transmitting and receiving point cloud data by encoding the data and transmitting it, followed by decoding and rendering at the reception end, utilizing techniques such as video-based point cloud compression (V-PCC) to address latency and complexity issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is transmitted using existing technologies, then the data can be transmitted, but the latency is high and encoding/decoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple blocks or partitions, allowing parallel processing during encoding and decoding. This segmentation reduces the computational complexity for each individual block while maintaining overall data fidelity, thereby addressing both the latency and complexity issues simultaneously
Solution Approach 2:
Prediction techniques are applied beforehand during encoding to estimate point cloud characteristics, and these predictions are utilized during decoding to reduce computational burden. This preliminary action reduces both encoding time and decoding complexity, effectively lowering latency while simplifying the overall processing requirements
2Measurement precision
If the number of points in 3D space is large, then the point cloud representation is more accurate, but data generation and transmission become more difficult
Solution Approach 1:
Only the essential or significant points are extracted and transmitted, rather than all points. This selective extraction maintains the accuracy needed for effective point cloud representation while significantly reducing the data volume that requires processing and transmission, thus improving productivity without sacrificing measurement precision
Solution Approach 2:
The precision or density of point cloud data is adjusted dynamically based on application requirements, viewing distance, or importance of regions. This parameter change allows the system to maintain high accuracy where needed while reducing data complexity in less critical areas, optimizing the balance between accuracy and transmission efficiency
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 the bitstream including the point cloud data; and decoding the point cloud data.