Point Cloud Data Encoding and Decoding via Tile Segmentation
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient, leading to latency and increased complexity in encoding and decoding, which hinders the delivery of high-quality point cloud services for applications like VR, AR, and self-driving services.
Innovation Solution
A method and device for efficiently processing point cloud data by encoding geometry and attribute information and transmitting a bitstream, which includes decoding the received bitstream to reconstruct the point cloud data, thereby reducing latency and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is processed using existing methods, then the processing can be performed, but the processing efficiency is low and latency is high
Solution Approach 1:
The patent segments point cloud data into multiple tiles or patches, allowing parallel processing of different regions. This division enables simultaneous encoding and decoding operations across multiple processing units, significantly improving processing efficiency and reducing overall latency compared to processing the entire point cloud as a single unit.
Solution Approach 2:
The patent performs preliminary organization and preprocessing of point cloud data into structured formats (such as organized point clouds with regular spacing) before encoding. This preliminary action prepares the data in advance for more efficient processing during transmission and decoding, reducing the computational burden and time required during critical transmission and rendering phases.
2Productivity
If point cloud data is processed using existing methods, then the processing can be performed, but the encoding and decoding complexity is high
Solution Approach 1:
The patent transforms point cloud data parameters by reorganizing them into structured formats with regular spacing patterns. This parameter change converts irregular point cloud representations into organized structures that are more amenable to efficient encoding and decoding algorithms, reducing computational complexity while maintaining processing efficiency.
Solution Approach 2:
The patent creates simplified representations or proxies of the original point cloud data during encoding, such as downsampled versions or structured intermediate representations. These copies enable faster decoding operations while preserving essential geometric and attribute information, thereby reducing decoding complexity without significantly compromising output quality.
Data Source
AI summary
A method for processing point cloud data according to embodiments may encode and transmit point cloud data. The method for processing point cloud data according to embodiments may receive and decode point cloud data.