Point Cloud Transmission With Predictive Compression for Lower Latency
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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 such as virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for efficiently transmitting and receiving point cloud data through encoding and decoding processes, utilizing geometry-based and video-based point cloud compression techniques, along with predictive tree structures for inter-frame prediction, to reduce latency and improve encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is transmitted using traditional compression methods, then data transmission is achieved, but latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is segmented into multiple blocks or regions, allowing parallel processing during encoding and decoding. This segmentation reduces the computational complexity for each individual block while maintaining overall data fidelity, thereby reducing both latency and encoding/decoding complexity.
Solution Approach 2:
The patent applies predictive coding techniques where prediction models are pre-computed or pre-positioned to predict future point cloud data based on historical data. This preliminary action reduces the amount of actual data that needs to be encoded and transmitted in real-time, significantly reducing latency and computational complexity.
2Manufacturing precision
If tens of thousands to hundreds of thousands of point data are used to represent point cloud content, then high-quality three-dimensional representation is achieved, but processing efficiency decreases
Solution Approach 1:
The patent applies different processing strategies to different regions of the point cloud data based on their importance and characteristics. Critical regions with high detail requirements are processed with higher precision, while less critical regions use simplified processing. This local quality approach maintains high overall representation quality while significantly improving processing efficiency.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as point density, compression ratios, and prediction model complexity based on the specific characteristics of the point cloud data and real-time performance requirements. This allows the system to maintain high representation quality when needed while switching to more efficient processing modes when latency is critical.
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 comprising the point cloud data. In addition, a point cloud data transmission device according to embodiments may comprise: an encoder which encodes point cloud data; and a transmitter which transmits a bitstream comprising the point cloud data.