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Noise‐Adaptive Shape Reconstruction from Raw Point Sets
Author(s) -
Giraudot Simon,
CohenSteiner David,
Alliez Pierre
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12189
Subject(s) - noise (video) , outlier , algorithm , computer science , mathematics , submanifold , quadratic equation , function (biology) , point (geometry) , graph , dimension (graph theory) , artificial intelligence , geometry , mathematical analysis , combinatorics , theoretical computer science , image (mathematics) , evolutionary biology , biology
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.