Point Cloud Data Encoding via Octree Segmentation
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Solution Overview
Problem
Existing methods for processing point cloud data are inefficient, leading to latency and 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, transmitting the encoded data as a bitstream, and decoding it for rendering, utilizing techniques such as geometry-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to process point cloud data, then the data can be processed, but the processing efficiency is low and latency is high
Solution Approach 1:
The patent segments point cloud data into multiple octree levels, allowing different parts of the data to be processed at different rates and priorities. This enables efficient processing by handling only the necessary portions of the point cloud, reducing overall processing time and latency while maintaining quality where needed.
Solution Approach 2:
The patent implements dynamic processing where the encoding and decoding processes can adapt to different quality requirements and available computational resources in real-time. The system dynamically adjusts processing parameters based on the specific needs of the application, improving efficiency without fixed processing pipelines.
2Productivity
If traditional encoding/decoding methods are used, then point cloud data can be transmitted, but the encoding/decoding complexity is high
Solution Approach 1:
The patent divides the encoding/decoding process into separate octree level processing stages, where each level can be handled independently. This segmentation reduces the complexity of any single processing step while maintaining the ability to transmit complete point cloud data through the hierarchical structure.
Solution Approach 2:
The patent uses a nested octree structure where lower-level detail information is contained within higher-level structures. This nesting allows the decoder to process only the necessary levels of detail, reducing decoding complexity while preserving the ability to reconstruct complete point cloud data when needed.
Data Source
AI summary
A point cloud data processing method according to embodiments may comprise the steps of: encoding point cloud data including geometry information and attribute information; and transmitting a bitstream including the encoded point cloud data.