Noise-Adaptive Shape Reconstruction from Raw Point Sets

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, whic...

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Bibliographic Details
Published inComputer graphics forum Vol. 32; no. 5; pp. 229 - 238
Main Authors Giraudot, Simon, Cohen-Steiner, David, Alliez, Pierre
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.08.2013
Wiley
Subjects
Algebraic Geometry
Algorithms
Computer graphics
Computer science
Confidence
Graphs
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Boundary representations
Mathematical analysis
Mathematical models
Mathematics
Noise
Reconstruction
Seeds
Studies
United States > US
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Summary: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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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12189