Proceedings Eurographics/IEEE VGTC Symposium Point-Based Graphics (2005)

Stony Brook, NY, USA

June 20, 2005 to June 21, 2005

ISSN: 1511-7813

ISBN: 3-905673-20-7

pp: 71-144

A. Belyaev , Max-Planck-Inst. fur Inf., Saarbrucken, Germany

H.-P. Seidel , Max-Planck-Inst. fur Inf., Saarbrucken, Germany

O. Schall , Max-Planck-Inst. fur Inf., Saarbrucken, Germany

ABSTRACT

In this paper, we develop a method for robust filtering of a noisy set of points sampled from a smooth surface. The main idea of the method consists of using a kernel density estimation technique for point clustering. Specifically, we use a mean-shift based clustering procedure. With every point of the input data we associate a local likelihood measure capturing the probability that a 3D point is located on the sampled surface. The likelihood measure takes into account the normal directions estimated at the scattered points. Our filtering procedure suppresses noise of different amplitudes and allows for an easy detection of outliers, which are then automatically removed by simple thresholding. The remaining set of maximum likelihood points delivers an accurate point-based approximation of the surface. We also show that while some established meshing techniques often fail to reconstruct the surface from original noisy point scattered data, they work well in conjunction with our filtering method.

INDEX TERMS

outlier detection, robust filtering, noisy scattered point data, kernel density estimation, point clustering, mean-shift based clustering, local likelihood measure, probability, noise suppression, simple thresholding, maximum likelihood point, point-based approximation, meshing technique, computational geometry, object modeling

CITATION

A. Belyaev,
H.-P. Seidel,
O. Schall,
"Robust filtering of noisy scattered point data",

*Proceedings Eurographics/IEEE VGTC Symposium Point-Based Graphics*, vol. 00, no. , pp. 71-144, 2005, doi:10.1109/PBG.2005.194067