Point Cloud Transmission With Octree Geometry Compression
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data, particularly in terms of latency, encoding/decoding complexity, and compression efficiency, which are critical for applications such as virtual reality, augmented reality, and autonomous driving.
Innovation Solution
A method and apparatus for point cloud data transmission and reception that utilize an octree-based coding method, incorporating motion estimation and compensation, to efficiently compress and transmit point cloud data by encoding geometry and attribute data, and performing spatially adaptive segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional point cloud data transmission methods are used, then the system is simple to implement, but compression efficiency is poor and data size is large
Solution Approach 1:
The patent applies octree-based spatial segmentation to divide the 3D point cloud space into hierarchical levels. Each octree node represents a spatial region that can be independently processed, allowing selective compression based on point density and distribution. This segmentation enables efficient compression by processing only occupied regions while maintaining simple implementation through standard tree traversal algorithms.
2Productivity
If motion estimation and compensation are applied to geometry data, then compression efficiency improves, but encoding time increases
Solution Approach 1:
The patent implements local motion estimation within each octree node or prediction unit, rather than performing global motion estimation. This allows motion vectors to be calculated only for regions with significant motion, reducing computational complexity. The system adapts the motion estimation strength based on local point cloud characteristics, achieving good compression efficiency without excessive encoding time.
3Quantity of substance
If point cloud data is transmitted without compression, then transmission speed is maintained, but bandwidth consumption is high
Solution Approach 1:
The patent implements dynamic compression where the compression level and method are adjusted based on real-time conditions including network bandwidth, processing capability, and point cloud characteristics. The system can switch between different compression modes (e.g., lossless for high bandwidth availability, lossy for bandwidth-constrained scenarios), maintaining optimal transmission speed while reducing data size as needed.
4Ease of manufacture
If geometry data is encoded without considering point distribution, then encoding process is simple, but compression efficiency is reduced
Solution Approach 1:
The patent dynamically adjusts encoding parameters such as quantization step size, prediction mode selection, and octree depth based on local point distribution characteristics. Regions with high point density receive finer quantization and more sophisticated prediction, while sparse regions use coarser encoding. This parameter adaptation achieves high compression efficiency while maintaining relatively simple encoding through automated parameter selection.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding geometry data including positions of points of point cloud data; encoding, on the basis of the geometry data, attribute data including attribute values of the points of the point cloud data; and transmitting the encoded geometry data, the encoded attribute data, and signaling information, wherein, in the step of encoding the geometry data, the geometry data can be compressed on the basis of the correlation between frames and the distribution of the points.