Authors: Hao Song, Hsi-Yung Feng
Addresses: Department of Mechanical and Materials Engineering, The University of Western Ontario, London, Ontario N6A 5B9, Canada. ' Department of Mechanical Engineering, The University of British Columbia, 6250 Applied Science Lane, Vancouver, B.C. V6T 1Z4, Canada
Abstract: This paper presents a new method for point cloud simplification. The method searches for a subset of the original point cloud data such that the maximum geometric deviation between the original and simplified data sets is below a specified error bound. The underlying principle of the simplification process is to partition the original data set into piecewise point clusters and represent each cluster by a single point. By iteratively updating the partition and efficiently evaluating the resulting geometric deviations, the proposed method is able to yield a simplified point cloud that satisfies the error bound constraint and contains near minimum number of data points.
Keywords: point cloud data; simplification; data reduction; geometric deviation; clustering; piecewise point clusters.
International Journal of Computer Applications in Technology, 2007 Vol.30 No.4, pp.236 - 244
Published online: 19 Feb 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article