Octree Point Cloud Encoding Reduces Latency
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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, especially in applications like VR and self-driving services.
Innovation Solution
A method and device for efficiently processing point cloud data by encoding the data and transmitting a bitstream containing the encoded data, utilizing techniques such as geometry-based and video-based point cloud compression, and employing octree structures and scalable attribute coding to reduce complexity and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional point cloud processing methods are used, then processing can be performed, but latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is segmented into multiple octants using an octree structure, where each octant represents a specific region in 3D space. This segmentation allows independent processing of different spatial regions, reducing overall processing complexity while maintaining high productivity through parallelizable operations on each octant.
Solution Approach 2:
The patent transforms the 3D point cloud data into a hierarchical octree structure, adding a dimensional organization layer. Points are mapped to octant indices and stored in a structured format that enables efficient querying and processing, reducing both latency and computational complexity compared to conventional flat storage methods.
2Manufacturing precision
If more point data is used to represent point cloud content, then quality improves, but processing latency and complexity increase
Solution Approach 1:
The patent performs preliminary organization of point cloud data into an octree structure during encoding, pre-computing octant indices and spatial relationships. This preliminary action enables rapid decoding and rendering by eliminating the need for complex spatial calculations during real-time processing, thus maintaining high quality with reduced latency.
Solution Approach 2:
The patent changes the organizational parameters of point cloud data from conventional coordinate-based storage to octant-indexed hierarchical storage. This parameter change enables efficient spatial queries and processing operations, allowing high-quality representation of large numbers of points without proportionally increasing processing latency.
3Productivity
If conventional encoding methods are used, then data can be transmitted, but transmission efficiency and service quality are reduced
Solution Approach 1:
The patent changes the encoding parameters by representing points using octant indices and hierarchical offsets instead of full 3D coordinates. This parameter transformation reduces the average number of bits required per point while maintaining spatial precision, thereby improving transmission efficiency without compromising service quality for VR and self-driving applications.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.