Noise-Adaptive Shape Reconstruction from Raw Point Sets

Simon Giraudot 1 David Cohen-Steiner 2 Pierre Alliez 1
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
2 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
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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Simon Giraudot, David Cohen-Steiner, Pierre Alliez. Noise-Adaptive Shape Reconstruction from Raw Point Sets. Computer Graphics Forum, Wiley, 2013, 32 (5), pp.229-238. ⟨http://diglib.eg.org/EG/CGF/volume32/issue5/v32i5pp229-238.pdf⟩. ⟨10.1111/cgf.12189⟩. ⟨hal-00844472⟩

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