Point Cloud Data Transmission Using Octree LOD Segmentation
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Solution Overview
Problem
The processing of large point cloud data for applications like virtual reality, augmented reality, and self-driving services is hindered by high latency and complexity in encoding and decoding, requiring efficient methods to transmit and receive this data effectively.
Innovation Solution
The method involves encoding geometry and attribute information using an octree structure, quantizing geometry, approximating the octree structure, and entropy-encoding occupancy codes, while matching attribute information with geometry information to generate levels of detail (LODs) for efficient compression and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality, then the service quality is improved, but the data volume and transmission bandwidth requirements increase
Solution Approach 1:
The point cloud data is segmented into multiple levels of detail (LODs), where each level represents a different degree of geometric and attribute information refinement. This segmentation allows receivers to selectively decode only the necessary level based on their capabilities and network conditions, reducing overall data transmission requirements while maintaining service quality for diverse applications
Solution Approach 2:
The patent employs parameter changes by transforming point cloud data into different representation forms including geometry information, attribute information, and occupancy codes. By encoding these parameters efficiently using octree structures and entropy coding, the data volume is significantly reduced while preserving the essential characteristics needed for high-quality point cloud services
2Quantity of substance
If point cloud data is compressed heavily, then the data volume is reduced, but the encoding and decoding complexity increases
Solution Approach 1:
The encoding process is segmented into distinct modules: octree structure generation, occupancy code extraction, geometry information encoding, and attribute information encoding. Each module handles a specific aspect of compression independently, which simplifies the overall complexity by breaking down the complex compression task into manageable, standardized steps that can be implemented efficiently
Solution Approach 2:
The patent performs preliminary organization of point cloud data into octree structures before compression. This preliminary action establishes a hierarchical framework that simplifies subsequent encoding operations, as the octree structure pre-organizes spatial relationships and identifies redundant information, thereby reducing the computational burden during actual compression and decoding processes
3Loss of time
If real-time transmission is implemented, then the latency is reduced, but the encoding speed requirements increase
Solution Approach 1:
The point cloud data processing is segmented into independent encoding streams for different levels of detail. This segmentation enables parallel processing of multiple LODs simultaneously, significantly increasing encoding throughput and speed to meet real-time transmission requirements without compromising compression efficiency
Solution Approach 2:
The patent performs preliminary organization of point cloud data into octree structures and pre-computes occupancy codes before final geometry and attribute encoding. This preliminary action prepares the data in an optimized format that accelerates the subsequent encoding process, enabling real-time transmission by reducing the computational time required for compression
Data Source
AI summary
A point cloud data transmission method according to embodiments can comprise the steps of: acquiring the point cloud data; encoding geometry information included in the point cloud data; encoding attribute information included in the point cloud data; and transmitting the encoded geometry information and the encoded attribute information.