G-PCC Point Cloud Tracks for Temporal Scalability and Low Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently processing large volumes of point cloud data, managing latency and encoding/decoding complexity, and supporting temporal scalability in geometry-based point cloud compressed data (G-PCC) for applications like VR and self-driving services.
Innovation Solution
A method and device for processing point cloud data that includes acquiring and reconstructing temporal scalability information from G-PCC files, generating G-PCC files with temporal scalability information, and dividing bitstreams into multiple tracks for efficient storage and access, thereby reducing signaling overhead and improving encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using traditional methods, then the data can be handled, but the processing efficiency is low and latency is high
Solution Approach 1:
The patent segments the G-PCC bitstream into multiple tracks, where each track carries specific components (geometry, attributes, or both). This segmentation allows parallel processing of different data components, improving processing efficiency and reducing latency by avoiding sequential handling of entire point cloud frames.
Solution Approach 2:
The patent introduces a new organizational dimension by mapping G-PCC components to multiple tracks instead of traditional single-track storage. This dimensional change enables efficient random access and parallel processing, transforming the data structure from a monolithic format to a multi-dimensional track-based format that improves processing throughput.
2Adaptability or versatility
If temporal scalability information is included in all tracks, then complete temporal scalability is achieved, but signaling overhead increases
Solution Approach 1:
The patent applies local quality by including temporal scalability information selectively in specific tracks based on their content. Tracks carrying geometry data include temporal scalability information, while tracks carrying only attributes may reference the geometry track's scalability information. This localized approach reduces redundant signaling while maintaining complete temporal scalability functionality.
Solution Approach 2:
The patent uses a reference mechanism where attribute tracks can reference the temporal scalability information from geometry tracks. This intermediary approach allows temporal scalability to be defined once in the geometry track and reused by attribute tracks, reducing signaling overhead while maintaining adaptability.
3Ease of manufacture
If point cloud data is stored in a single track, then file structure is simple, but access efficiency and encoding/decoding performance are reduced
Solution Approach 1:
The patent segments the G-PCC bitstream into multiple tracks organized by component type (geometry tracks and attribute tracks). This segmentation improves encoding/decoding efficiency by allowing processors to handle specific component types in parallel and enables efficient random access to particular components without processing the entire bitstream.
Solution Approach 2:
The patent transforms the single-track structure into a multi-dimensional track-based structure where tracks are organized by component type. This dimensional change enables efficient access patterns and parallel processing while maintaining a relatively simple file structure through standardized track organization and metadata.
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 be acquired from one or more tracks, and temporal scalability information of a first track may be derived from temporal scalability information of a second track based on a component carried by the first track.