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          Institute: MPI für Informatik     Collection: Computer Graphics Group     Display Documents

ID: 278979.0, MPI für Informatik / Computer Graphics Group
Robust Filtering of Noisy Scattered Point Data
Authors:Schall, Oliver; Belyaev, Alexander; Seidel, Hans-Peter
Editors:Pauly, Mark; Zwicker, Matthias
Publisher:Eurographics Association
Place of Publication:Aire-la-Ville, Switzerland
Date of Publication (YYYY-MM-DD):2005
Title of Proceedings:IEEE/Eurographics Symposium on Point-Based Graphics
Start Page:71
End Page:77
Place of Conference/Meeting:Stony Brook, New York, USA
(Start) Date of Conference/Meeting
Review Status:not specified
Audience:Experts Only
Intended Educational Use:No
Abstract / Description: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.
Last Change of the Resource (YYYY-MM-DD):2006-05-18
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Hans-Peter Seidel
Affiliations:MPI für Informatik/Computer Graphics Group
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