Point Cloud Temporal Tracks for Scalable G-PCC Reconstruction
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large volumes of point cloud data for applications like virtual reality, augmented reality, and self-driving services, with issues related to latency, encoding/decoding complexity, and the need for temporal scalability in geometry-based point cloud compressed data (G-PCC).
Innovation Solution
A method and device for processing point cloud data that supports temporal scalability by defining interleaving between samples of different temporal levels, allowing efficient storage and access to G-PCC bitstreams, and improving encoding/decoding efficiency through a clear track system for multi-track content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using existing technologies, then the data can be transmitted and reconstructed, but the processing complexity and latency increase significantly
Solution Approach 1:
The patent segments point cloud data into multiple tracks based on temporal levels, where each track contains samples with identical temporal level identifiers. This segmentation allows parallel processing of different temporal layers, reducing overall processing complexity while maintaining high productivity through efficient division of the large data volume into manageable, independently processable units.
2Adaptability or versatility
If temporal scalability is supported in G-PCC, then the system can serve multiple quality levels, but the data structure and access methods become more complex
Solution Approach 1:
The patent introduces a temporal level dimension to organize point cloud samples, creating a multi-dimensional structure where samples are arranged not only spatially but also temporally. By adding this temporal dimension with discrete level identifiers, the system achieves temporal scalability while managing complexity through systematic organization rather than arbitrary data structures.
Solution Approach 2:
The patent enables dynamic adaptation to different playback requirements by allowing receivers to selectively process tracks based on their temporal level capabilities. The system can dynamically adjust which tracks are decoded and reconstructed based on available bandwidth, processing power, and quality requirements, providing versatile temporal scalability without requiring complex static data structures.
3Productivity
If interleaving between samples of different temporal levels is defined, then efficient storage and access is achieved, but the encoding process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-defining the interleaving pattern between samples of different temporal levels during the encoding phase. By establishing a predetermined alternating sequence (e.g., low temporal level sample, then high temporal level sample, and so on), the system prepares the data structure in advance to enable efficient storage and access operations, reducing the computational burden during playback while managing encoding complexity through systematic pre-processing.
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 include a first syntax element for an identifier of a temporal level of a sample in a temporal level track, and wherein an identifier value of the temporal level may be expressed as a discrete value.