Noise‐Adaptive Shape Reconstruction from Raw Point Sets. (19th August 2013)

• Record Type: Journal Article
• Authors: Giraudot, Simon; Cohen‐Steiner, David; Alliez, Pierre
• Is Part Of: Computer graphics forum. Volume 32:Number 5(2013)
• Page Start: 229
• Page End: 238
• Publication Date: 2013-08-19
• Subjects: Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.12189
• Languages: English
• ISSNs: 0167-7055

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>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.</p> </abstract>