Point-cloud simplification with bounded geometric deviations

Authors:
Hao Song;Hsi-Yung Feng
Affiliations:
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
Venue:
International Journal of Computer Applications in Technology
Year:
2007

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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.