Curvature and density based feature point detection for point cloud data
Curvature and density based feature point detection for point cloud data
- Author(s): Lihui Wang and Baozong Yuan
- DOI: 10.1049/cp.2010.0694
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- Author(s): Lihui Wang and Baozong Yuan Source: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010), 2010 p. 377 – 380
- Conference: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010)
- DOI: 10.1049/cp.2010.0694
- ISBN: 978-1-84919-240-8
- Location: Beijing, China
- Conference date: 26-29 Sept. 2010
- Format: PDF
Information of unordered point cloud is limited because of no direct topologic relation between points or triangular facets. So it will be difficult to obtain the feature points of 3D point cloud data. In this article, we use the geometry properties, such as normal, curvature and density of the points' information to detect features of the 3D point cloud data and propose a curvature and density based feature point detection method for unordered 3D point cloud data. Firstly, we define a feature parameter of 3D point cloud data, which includes the distance with its neighboring points, the sum of the normal angle between the point and neighboring points, and point cloud data curvature. Secondly, the density of data points is calculated by using Octree and is used as the features of points by a threshold of their feature parameter. The experimental results show that our new approach might detect feature points accurately for the given 3D point cloud data.
Inspec keywords: geometry; feature extraction; octrees
Subjects: Combinatorial mathematics; Computer vision and image processing techniques; Combinatorial mathematics; Optical, image and video signal processing
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