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Denoising Manifold and Non-Manifold Point Clouds

Ranjith Unnikrishnan and Martial Hebert
Conference Paper, Carnegie Mellon University, 18th British Machine Vision Conference (BMVC), September, 2007

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Abstract

The faithful reconstruction of 3-D models from irregular and noisy point samples is a task central to many applications of computer vision and graphics. We present an approach to denoising that naturally handles intersections of manifolds, thus preserving high-frequency details without oversmoothing. This is accomplished through the use of a modified locally weighted regression algorithm that models a neighborhood of points as an implicit product of linear subspaces. By posing the problem as one of energy minimization subject to constraints on the coefficients of a higher order polynomial, we can also incorporate anisotropic error models appropriate for data acquired with a range sensor. We demonstrate the effectiveness of our approach through some preliminary results in denoising synthetic data in 2-D and 3-D domains.

BibTeX Reference
@conference{Unnikrishnan-2007-9810,
title = {Denoising Manifold and Non-Manifold Point Clouds},
author = {Ranjith Unnikrishnan and Martial Hebert},
booktitle = {18th British Machine Vision Conference (BMVC)},
sponsor = {Army Research Laboratory},
grantID = {DAAD19-01-209912},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2007},
address = {Pittsburgh, PA},
}
2017-09-13T10:42:02+00:00