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Carnegie Mellon Robotics Institute

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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.

Notes

Sponsor: Army Research Laboratory

Grant ID: DAAD19-01-209912

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Number of pages: 10

Text Reference

Ranjith Unnikrishnan and Martial Hebert, "Denoising Manifold and Non-Manifold Point Clouds," 18th British Machine Vision Conference (BMVC), September, 2007.

BibTeX Reference

@inproceedings{Unnikrishnan_2007_5833,
   author = "Ranjith Unnikrishnan and Martial Hebert",
   title = "Denoising Manifold and Non-Manifold Point Clouds",
   booktitle = "18th British Machine Vision Conference (BMVC)",
   month = "September",
   year = "2007",
}

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