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-based occupancy codes.
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 and universally applicable, but the compression efficiency is poor for point clouds with explicit geometrical structures
Solution Approach 1:
The patent segments the point cloud data into two distinct parts: geometric structure components and residual components. The geometric structure is extracted and encoded separately using appropriate geometric primitives, while the residuals capture the remaining variations. This segmentation allows each part to be encoded with the most suitable method, resolving the contradiction between simplicity and compression efficiency.
Solution Approach 2:
Instead of directly encoding the raw point cloud data as done in traditional octree-partition methods, the patent inverts the approach by first extracting and encoding the geometric structure, then encoding the residuals. This inversion allows the method to leverage the regularity of geometric structures for better compression while maintaining simplicity through modular processing.
2Reliability
If octree-partition with occupancy codes is used, then the geometry information can be coded, but spatial linearity and geometric features are not effectively utilized
Solution Approach 1:
The patent changes the encoding parameters by introducing geometric structure-specific parameters such as line equations, plane equations, and curve representations. Instead of using uniform occupancy codes for all point clouds, the method adapts the encoding parameters to match the underlying geometric features, thereby effectively utilizing spatial linearity and improving both reliability and compression efficiency.
3Loss of information
If geometric features are detected and represented using mathematical equations, then compression ratio increases, but processing complexity increases
Solution Approach 1:
The patent applies partial geometric feature detection by focusing on the most prominent geometric structures in the point cloud rather than attempting to model every detail. By detecting and encoding only the significant geometric features (such as dominant lines, planes, and curves), the method achieves high compression ratios while limiting processing complexity to what is necessary for the most important structures.
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.