An unsupervised point cloud segmentation method and system
By employing PCA analysis and an unsupervised point cloud segmentation method with no directional normals, the problem of point cloud segmentation under unlabeled datasets is solved, achieving efficient and low-cost segmentation results.
CN117392151BActive Publication Date: 2025-10-21NINGDE SKEQI INTELLIGENT EQUIP CO LTD
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Patent Information
- Application Number
- CN202311358480.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-19
- Publication Date
- 2025-10-21
- Estimated Expiration
- 2043-10-19
AI Technical Summary
Technical Problem
Existing point cloud segmentation technologies require labeled datasets, which leads to high costs. How can point cloud segmentation be achieved using unlabeled datasets to reduce costs?
Method used
The neighborhood radius is estimated by calculating eigenvalues through PCA analysis, point cloud segmentation is performed using undirected normals, and region growth and edge point refinement are carried out by seed growth to extract the neighborhood relationship graph.
Benefits of technology
Point cloud segmentation can be achieved in a short time with low computational cost, avoiding reliance on training data and manual intervention, and improving segmentation efficiency and accuracy.
✦ Generated by Eureka AI based on patent content.
Abstract
The application relates to an unsupervised point cloud segmentation method and system, and relates to the technical field of target segmentation, and comprises the following steps: S1, acquiring point cloud data, estimating a neighborhood radius, and making the ratio of a first target eigenvalue and a second target eigenvalue not less than a preset value when a directionless normal of each point is extracted based on PCA; S2, acquiring a local representative subset through range search based on the estimated neighborhood radius; estimating a local tangent plane of each point in the local representative subset based on PCA, and extracting a directionless normal of each point based on the local tangent plane of each point; and S3, performing plane segmentation based on the directionless normal of each point. The neighborhood radius is estimated by calculating eigenvalues based on PCA analysis, so that the plane information in original data is fully utilized when the normal of each point is estimated. The unsupervised system does not need training data or manual intervention, and can obtain a point cloud segmentation result in a short time and at a low calculation cost.
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