Curvature and Density based Feature Point Detection for Point Cloud Data
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.
3D point cloud data unordered feature parameter feature point detection K nearest neighbors
Lihui Wang Baozong Yuan
Institute of Information Science, Beijing Jiaotong University, Beijing, China
国际会议
北京
英文
377-380
2010-09-26（万方平台首次上网日期，不代表论文的发表时间）