Noise-Adaptive Shape Reconstruction from Raw Point Sets
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and Blackwell Publishing Ltd.
Abstract
We propose a noise-adaptive shape reconstruction method specialized to smooth, closed shapes. Our algorithm takes as input a defect-laden point set with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that the inferred shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points computed in previous step.
Description
@article{10.1111:cgf.12189,
journal = {Computer Graphics Forum},
title = {{Noise-Adaptive Shape Reconstruction from Raw Point Sets}},
author = {Giraudot, Simon and Cohen-Steiner, David and Alliez, Pierre},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.12189}
}