Noise-Adaptive Shape Reconstruction from Raw Point Sets
| dc.contributor.author | Giraudot, Simon | en_US |
| dc.contributor.author | Cohen-Steiner, David | en_US |
| dc.contributor.author | Alliez, Pierre | en_US |
| dc.contributor.editor | Yaron Lipman and Hao Zhang | en_US |
| dc.date.accessioned | 2015-02-28T15:51:40Z | |
| dc.date.available | 2015-02-28T15:51:40Z | |
| dc.date.issued | 2013 | en_US |
| dc.description.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. | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.identifier.doi | 10.1111/cgf.12189 | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12189 | en_US |
| dc.publisher | The Eurographics Association and Blackwell Publishing Ltd. | en_US |
| dc.subject | I.3.5 [Computer Graphics] | en_US |
| dc.subject | Computational Geometry and Object Modeling | en_US |
| dc.subject | Boundary representations | en_US |
| dc.title | Noise-Adaptive Shape Reconstruction from Raw Point Sets | en_US |