Point Cloud Bitstream Packaging for Low-Latency Tile Access
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently transmitting and receiving large amounts of data, particularly in addressing latency and encoding/decoding complexity, as well as in storing and accessing geometry-point cloud compression (G-PCC) bitstreams effectively.
Innovation Solution
A method and apparatus for encoding point cloud data, encapsulating it into a file with signaling data, and transmitting it, allowing storage in either a single track or multiple tracks, with efficient access and decoding capabilities, utilizing Type-Length-Value (TLV) encapsulation structures and spatial partitioning of point cloud data into tiles and slices for improved encoding and decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is transmitted using conventional methods, then data can be transmitted, but transmission efficiency is low and latency is high due to large data volume
Solution Approach 1:
The point cloud data is divided into multiple tiles, where each tile represents a specific region of the three-dimensional space. This segmentation allows for parallel processing and selective transmission of only relevant tile data, significantly reducing the overall data transmission volume and latency while maintaining high transmission efficiency
Solution Approach 2:
The patent extracts and transmits only the necessary parameter sets and signaling data required for decoding specific tiles, rather than transmitting complete point cloud data structures. This selective extraction reduces redundant data transmission and improves transmission efficiency
2Ease of manufacture
If point cloud data is encoded using conventional methods, then encoding can be performed, but encoding and decoding complexity is high
Solution Approach 1:
The encoding process is segmented into tile-based processing units, allowing independent encoding of each tile with its own parameter sets. This segmentation reduces the overall encoding complexity by breaking down large-scale processing into manageable units and enables parallel decoding operations
Solution Approach 2:
The patent uses parameter sets to control and optimize encoding/decoding operations. By dynamically selecting and transmitting only the necessary parameter sets for each tile, the complexity of encoding and decoding is reduced while maintaining flexibility in processing different regions of the point cloud data
3Productivity
If G-PCC bitstream is stored in conventional file structures, then storage is possible, but access efficiency is low
Solution Approach 1:
The G-PCC bitstream is organized into a file structure where data is divided into samples, tracks, and tiles. This hierarchical segmentation enables efficient random access to specific tiles and parameter sets without requiring processing of the entire bitstream, significantly improving access efficiency
Solution Approach 2:
The patent introduces signaling data as an intermediary layer between the raw G-PCC bitstream and the decoding process. This signaling data contains metadata and parameter information that facilitates efficient navigation and access to specific regions of the bitstream, reducing access complexity
Data Source
AI summary
According to embodiments, point cloud data transmission method may include encoding point cloud data, encapsulating a bitstream that includes the encoded point cloud data and signaling data into a file, and transmitting the file, the bitstream is stored either in a single track or in multiple tracks of the file, the signaling data include at least one parameter set, and the encoded point cloud data include a geometry bitstream containing geometry data and an attribute bitstream containing attribute data.