GEOMETRIC ATTRIBUTE ANALYSIS AND

SEGMENTATION OF POINT CLOUD

KE Yinglin  CHEN Xi

(College of Mechanical and Energy Engineering, Zhejiang University, Hangzhou 310027)

Abstract: To improve the efficiency of reverse modeling, an automatic segmentation algorithm based on geometric attribute analysis is proposed. The algorithm subdivides point cloud into cubic grids and then maps the geometric attribute value of points in each grid to normal curvature coordinate system and Gaussian sphere. By testing hypothesis, the patterns of the normal curvature image and the Gaussian image are recognized. Based on the grid structure, the image points clustering, and the goodness-of-fit testing, point cloud is segmented into several regions and characterized as natural quadrics, extruded surfaces and ruled surfaces, respectively. Applications show that the proposed algorithm deals with large amount of measured points stably and effectively. It can be applied to many other fields including visual reality and computer vision, etc.

Key words:Region segmentation  Feature extraction  Point cloud  Reverse engineering

CLC No: TP391

国家863计划(863-511-942-018)和国家自然科学基金(50435020)资助项目.Received 20050622,received in revised form 20051218