Point Cloud Temporal Scalability for Low-Latency 3D Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies have not effectively addressed the challenges of processing and handling the complexity of point cloud data, particularly in applications such as VR, AR, and self-driving applications, and the need for improved methods to encode and decode point cloud data efficiently.
Innovation Solution
A method and device for efficiently processing point cloud data by implementing a system and method for processing point cloud data, particularly in applications such as VR, AR, and self-driving applications, and the need for improved methods to encode and decode point cloud data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using existing technologies, then the data can be handled, but the processing complexity and latency are high
Solution Approach 1:
The point cloud data is divided into multiple layers or levels of detail, allowing progressive transmission and decoding. This segmentation enables the system to process and render essential structures first, then progressively add finer details, thereby reducing overall processing complexity and latency while maintaining high productivity
Solution Approach 2:
The patent implements pre-processing and pre-organization of point cloud data into structured formats with predefined hierarchies and relationships. By performing preliminary organization of spatial and attribute data before transmission, the system reduces real-time processing complexity during decoding while improving processing efficiency during playback
2Adaptability or versatility
If temporal scalability is implemented in point cloud compression, then service quality is improved, but encoding complexity increases
Solution Approach 1:
The point cloud data stream is segmented into multiple temporal layers or levels, where each layer represents a different temporal resolution or frame rate. This allows the encoder to process base temporal layers first and then add enhancement layers, improving adaptability for different playback scenarios while managing encoding complexity through hierarchical processing
Solution Approach 2:
The patent implements dynamic temporal scalability where the encoding process can adaptively adjust the number of temporal layers and their characteristics based on available bitrate and processing resources. This dynamic approach enables versatile service quality adaptation while optimizing encoding complexity through conditional processing paths
3Manufacturing precision
If point cloud data is transmitted with high detail, then quality is improved, but transmission time and latency increase
Solution Approach 1:
The high-detail point cloud data is segmented into progressive levels of detail, allowing transmission of coarse structures first and progressively refining with additional data packets. This segmentation enables the system to achieve high quality point cloud reconstruction while minimizing initial latency by delivering essential information faster
Solution Approach 2:
The patent implements pre-computation and pre-organization of point cloud data into progressive detail levels with optimized spatial and attribute relationships. By performing preliminary organization of data at multiple quality levels before transmission, the system enables fast delivery of high-quality reconstructions while reducing latency through efficient data structure utilization
Data Source
AI summary
A transmission device of point cloud data, a method performed by the transmission device, a reception device and a method performed by the reception device are provided. A method performed by a reception device of point cloud data comprises acquiring temporal scalability information of a point cloud in a three-dimensional space based on a G-PCC file and reconstructing the three-dimensional point cloud based on the temporal scalability information. The temporal scalability information may comprise a first syntax element and a second syntax element for the three-dimensional point cloud, the first syntax element may indicate the number of temporal levels in a track, the second syntax element may indicate whether the three-dimensional point cloud is carried in multiple temporal level tracks, and the second syntax element may be determined based on a value of the first syntax element.