Point Cloud Transmission Weighted Morton Code Compression
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Solution Overview
Problem
Existing methods for transmitting and receiving point cloud data face challenges in efficiency, latency, and encoding/decoding complexity, particularly due to the large number of points in 3D space and the need for high throughput.
Innovation Solution
A point cloud data transmission and reception system that uses weighted Morton code generation for accurate spatial distance calculation, supporting increased compression rates and quality performance through adaptive coding methods, including geometry-based and video-based point cloud compression schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number of points in 3D space is large to provide high-quality point cloud data, then the quality and detail of the point cloud service is improved, but the data transmission complexity and encoding/decoding complexity increase significantly
Solution Approach 1:
The point cloud data is divided into multiple blocks or regions, and prediction is performed independently for each block. This segmentation reduces the computational complexity of encoding and decoding while maintaining high point cloud quality, as each smaller block requires less processing power than the entire point cloud dataset.
Solution Approach 2:
The patent applies different prediction parameters and methods for different blocks of point cloud data. By changing parameters such as prediction mode, block size, and attribute information selection based on local characteristics, the system achieves high quality reconstruction with reduced overall complexity.
2Device complexity
If prediction method is applied to limit the area and apply predicted attribute information without verification, then the encoding/decoding complexity is reduced, but the measurement precision and attribute accuracy deteriorate
Solution Approach 1:
The patent applies different prediction strategies to different regions based on their characteristics. For some blocks, simple prediction without verification is used to reduce complexity, while for other blocks requiring higher accuracy, verification procedures are applied. This local differentiation maintains attribute accuracy where needed while reducing overall complexity.
Solution Approach 2:
Instead of applying verification to all blocks (excessive action), the patent applies verification only to selected blocks where it is most beneficial (partial action). This selective approach reduces the overall encoding/decoding complexity while maintaining sufficient attribute accuracy for the majority of the point cloud data.
3Speed
If high throughput is required to transmit large point cloud data, then the data transmission speed is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The point cloud data is segmented into multiple blocks that can be processed and transmitted in parallel. This segmentation enables higher throughput by utilizing multiple processing channels simultaneously, achieving fast transmission speeds without requiring a single overly complex processing system.
Solution Approach 2:
Prediction and preprocessing operations are performed before the actual transmission and decoding stages. By preparing predicted attribute information in advance, the system reduces the processing burden during real-time transmission, enabling high throughput with manageable device complexity.
Data Source
AI summary
A point cloud data transmission device according to embodiments may comprise: an acquisition unit for acquiring point cloud data; an encoder for encoding the acquired point cloud data; and a transmitter for transmitting a bitstream including the encoded point cloud data. A point cloud data reception device according to embodiments may comprise: a receiver for receiving a bitstream including point cloud data; a decoder for decoding the point cloud data; and a renderer for rendering the point cloud data.