Point Cloud Geometry Compression With Predictive Octree Coding
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and transmitting large amounts of point cloud data, particularly in terms of latency, encoding/decoding complexity, and compression of geometry-point cloud compression bitstreams, which are essential for applications like virtual reality, augmented reality, and self-driving services.
Innovation Solution
A point cloud data transmission and reception method utilizing a predictive tree structure for geometry information compression, involving rearrangement of points, generation of predictive trees, and direct coding modes to efficiently compress and decompress point cloud data, allowing for low-latency applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If point cloud data is transmitted using traditional compression methods, then data transmission is achieved, but latency is high and encoding/decoding complexity is excessive
Solution Approach 1:
The point cloud data is segmented into multiple octrees based on spatial regions. Each octree represents a specific region and can be processed independently, allowing parallel encoding and decoding operations that reduce overall processing time and complexity
Solution Approach 2:
The patent transforms the traditional 3D point cloud representation into a multi-dimensional octree structure with additional hierarchical levels. This dimensional transformation enables more efficient data organization and compression, reducing latency while managing complexity through structured decomposition
2Measurement precision
If tens of thousands to hundreds of thousands of point data are used to represent point cloud content, then representation accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The large set of points is divided into multiple octrees, each containing a subset of points. This segmentation allows processing to be distributed across multiple data structures, maintaining representation accuracy while improving processing efficiency through parallel operations
Solution Approach 2:
The patent applies octree decomposition to create a hierarchical structure where not all points need to be processed at the same level of detail. Coarser representations can be used for distant regions while maintaining fine detail for nearby points, optimizing the balance between accuracy and processing efficiency
3Loss of substance
If a predictive tree structure is used for geometry information compression, then bitstream size is reduced, but processing complexity increases
Solution Approach 1:
The predictive tree processing is segmented by octree regions, allowing the compression algorithm to be applied locally to each region rather than globally. This reduces the computational complexity at each processing stage while still achieving significant bitstream size reduction through predictive coding
Solution Approach 2:
The patent performs preliminary organization of point cloud data into octree structures before applying predictive tree compression. This preliminary action simplifies subsequent compression operations by pre-organizing data in a hierarchical manner that facilitates efficient prediction and coding
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: acquiring point cloud data; encoding geometry information including the positions of points of the point cloud data; encoding, on the basis of the geometry information, attribute information including attribute values of the points of the point cloud data; and transmitting the encoded geometry information, the encoded attribute information, and signaling information.