Point Cloud Coding Using Geometric Feature Detection
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Solution Overview
Problem
Current point cloud compression technologies, such as octree-partition coding, are less efficient for point clouds with explicit geometrical structures, as they do not effectively utilize spatial linearity and other geometric features, leading to suboptimal data representation and compression.
Innovation Solution
The method involves detecting geometric features like lines, parabolas, and planes within the point cloud data and representing them using mathematical equations, allowing for more efficient encoding and decoding by treating these features directly rather than relying solely on octree partitioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If octree-partition coding is used for point cloud compression, then the method is simple to implement, but the compression efficiency is low for point clouds with explicit geometrical structures
Solution Approach 1:
The patent segments the point cloud into different geometric primitive types (planes, cylinders, cones, spheres) and applies specialized encoding methods to each type. This segmentation allows the system to exploit the specific geometric properties of each primitive, achieving better compression efficiency while maintaining implementation feasibility through modular processing.
Solution Approach 2:
The patent transforms the representation parameters from generic 3D coordinates to geometry-specific parameters (e.g., plane equations ax+by+cz+d=0, cylinder radius and height, cone apex and base parameters). This parameter transformation enables more efficient encoding by capturing the essential geometric characteristics with fewer bits, directly improving compression efficiency.
2Device complexity
If octree-partition coding is used for point cloud compression, then the coding process is straightforward, but spatial linearity and geometric features are not effectively utilized
Solution Approach 1:
The patent performs preliminary geometric feature detection and primitive classification before the actual compression coding. By identifying planes, cylinders, cones, and spheres in advance and fitting appropriate geometric models, the system preserves geometric feature accuracy while simplifying the subsequent coding process to merely encoding the fitted parameters rather than individual point coordinates.
Solution Approach 2:
The patent creates simplified geometric copies (mathematical surfaces) that approximate the original point cloud structures. Instead of encoding all original points, the system encodes the parameters of these simplified geometric surfaces (planes, cylinders, cones, spheres), which accurately represent the underlying geometric features with significantly reduced data while maintaining representation accuracy.
3Productivity
If traditional compression methods are used, then data transmission and storage are slower, but the compression algorithms are well-established
Solution Approach 1:
The patent dramatically reduces the data volume by changing from encoding individual point coordinates (3D position + attributes per point) to encoding geometric primitive parameters (a limited set of parameters defining planes, cylinders, cones, spheres). This parameter transformation achieves higher compression ratios, enabling faster data transmission and more efficient storage while preserving the essential geometric information.
Data Source
AI summary
A method, computer program, and computer system for point cloud coding is provided. Data corresponding to a point cloud is received, and one or more geometric features are detected from among the data corresponding to the point cloud. A representation is determined for one or more of the detected geometric features, and the received data is encoded or decoded based on the determined representations whereby the point cloud is reconstructed based on the decoded data.