期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：11
出版社：S.S. Mishra
摘要：This Acquisition system always is used for extracting 3D point clouds of a given object. This system is capable to obtain accurate 3D points that can represent the geometrical structure of the object. A major problem of this system is that certain types of camera or scanner produce vast amounts of data, the processing of which presents serious problems. Rather than process all of this data at every stage of the representation process, an alternative is to use a strategy in which the data is initially reduced, then a pre-processing can be completed without consuming a lot of time. This paper presents an algorithm for managing the amount of point data acquired by laser scanner. The proposed algorithm includes a method based on the surface fitting which is fundamental in the most of reverse engineering algorithms. The surface fitting is calculated by fitting the best fit surface to the neighbourhood. The neighbourhood is obtained by subdividing the point data into cells based on scanned surface structures. Thus, the amount of points can be reduced by sampling the representative points for each cell. Experimental results show that the proposed method has good results and appears to be quite stable even for large scale data reduction. The proposed algorithm is proved by fitting known shapes such as plane using the well-known least squares method. For better comparison, we apply two different projection patterns, we used random squares pattern, and uniform squares pattern. By computing the distance from each 3D point to the plane, the RMS error between the 3D points and the plane fitting is calculated. The experimental results show that the random squares pattern is more accurate and faster than the uniform squares pattern for getting 3D points cloud for scanning objects
关键词：Laser scanner; 3D points reduction; surface fitting; RMS error
