Point-cloud simplification with bounded geometric deviations
by Hao Song, Hsi-Yung Feng
International Journal of Computer Applications in Technology (IJCAT), Vol. 30, No. 4, 2007

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.

Online publication date: Tue, 19-Feb-2008

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