Point Cloud Temporal Scalability in G-PCC Files
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Solution Overview
Problem
Existing methods for processing point cloud data face challenges in efficiently handling large volumes of data, leading to latency and encoding/decoding complexity, particularly in supporting temporal scalability and efficient storage and access.
Innovation Solution
A method and device for processing point cloud data using geometry-based point cloud compression (G-PCC) that includes reconstructing point clouds based on temporal scalability information, determining whether temporal scalability is applied, and generating G-PCC files with temporal scalability information, allowing for efficient storage and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed without temporal scalability, then encoding/decoding complexity is reduced, but latency increases and processing efficiency decreases
Solution Approach 1:
The patent segments point cloud data into multiple temporal levels (e.g., base layer and enhancement layers) with different resolutions and quality levels. This allows the receiver to selectively decode only the necessary temporal levels based on network conditions and processing requirements, thereby reducing encoding/decoding complexity while maintaining processing efficiency through parallel processing of different temporal layers.
Solution Approach 2:
The patent implements dynamic temporal scalability where the system can adaptively adjust the temporal levels and quality parameters based on real-time network conditions and processing capabilities. This dynamic adjustment allows the system to optimize the balance between processing efficiency and complexity on a per-session basis, enabling efficient processing without fixed high complexity.
2Ease of operation
If point cloud data is stored in a single track, then storage simplicity is improved, but access efficiency and scalability are reduced
Solution Approach 1:
The patent divides the point cloud data into multiple tracks, where each track corresponds to a specific temporal level or quality layer. This segmentation allows the receiver to access and process only the necessary tracks based on requirements, significantly improving access efficiency while maintaining relatively simple storage structure through standardized track formatting.
Solution Approach 2:
The patent introduces an additional organizational dimension by creating multiple tracks that represent different temporal levels. This dimensional organization allows for efficient parallel access to different quality levels without complicating the basic storage mechanism, as each track maintains a consistent and simple data structure that is easily manageable.
3Adaptability or versatility
If temporal scalability information is not included, then signaling overhead is reduced, but service adaptability and quality are limited
Solution Approach 1:
The patent extracts temporal scalability information into a dedicated, standardized signaling structure that can be efficiently parsed and processed. By separating this metadata from the main point cloud data streams, the system minimizes the impact on overall signaling overhead while providing comprehensive service adaptability through structured temporal level descriptions.
Solution Approach 2:
The patent uses parameter-based signaling where temporal scalability information is encoded through a set of standardized parameters that describe the available temporal levels, quality attributes, and decoding requirements. This parameter-based approach provides extensive service adaptability while keeping signaling overhead minimal, as the parameters are efficiently packed and interpreted by the receiving system.
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 may comprise obtaining a geometry-based point cloud compression (G-PCC) file including the point cloud data and reconstructing the point cloud based on temporal scalability information. The temporal scalability information may include information about multiple temporal level tracks for the file, and the information about multiple temporal level tracks may be determined based on a sample entry type of a track.