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Journal Articles Computer Graphics Forum Year : 2013

Noise-Adaptive Shape Reconstruction from Raw Point Sets

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Simon Giraudot
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David Cohen-Steiner
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Pierre Alliez

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.
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Dates and versions

hal-00844472 , version 1 (15-07-2013)

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Simon Giraudot, David Cohen-Steiner, Pierre Alliez. Noise-Adaptive Shape Reconstruction from Raw Point Sets. Computer Graphics Forum, 2013, 32 (5), pp.229-238. ⟨10.1111/cgf.12189⟩. ⟨hal-00844472⟩

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