Point Cloud Bitstream Encoding for Lower-Latency Decoding
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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 virtual reality, augmented reality, and self-driving services.
Innovation Solution
A method and device for encoding and decoding point cloud data using bitstreams, incorporating techniques such as geometry-based and video-based point cloud compression, along with feedback information to optimize processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is transmitted using traditional encoding methods, then data can be transmitted, but latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is divided 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 system performs preliminary processing on point cloud data before transmission, including pre-computation of geometric relationships and prediction models. This preliminary action prepares the data in advance, reducing the computational burden during real-time decoding and lowering latency.
2Manufacturing precision
If more point data is used to represent point cloud content, then quality improves, but processing complexity increases
Solution Approach 1:
The system dynamically adjusts encoding parameters such as quantization precision, transformation depth, and prediction model complexity based on the required output quality and available computational resources. This allows high-quality representation with adaptive control over processing complexity.
Solution Approach 2:
Instead of transmitting and processing all original point data, the system uses predictive models to generate approximate representations of point cloud data. These models create simplified copies or predictions that capture essential geometric features while reducing the number of points that need to be explicitly processed.
3Reliability
If point cloud data is processed in real-time for VR and self-driving services, then service quality improves, but latency increases
Solution Approach 1:
The system processes point cloud data in periodic frames or time intervals optimized for the specific application (VR or self-driving). This periodic processing allows for efficient batch operations while meeting real-time requirements, balancing service quality with latency constraints.
Solution Approach 2:
The system performs preliminary processing on point cloud data before transmission, including pre-computation of geometric relationships and prediction models. This preliminary action prepares the data in advance, reducing the computational burden during real-time decoding and lowering latency.
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. 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 including the point cloud data.