Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype
dc.contributor.author | Huang, Qi-Xing | en_US |
dc.contributor.author | Adams, Bart | en_US |
dc.contributor.author | Wand, Michael | en_US |
dc.contributor.editor | Alexander Belyaev and Michael Garland | en_US |
dc.date.accessioned | 2014-01-29T09:43:15Z | |
dc.date.available | 2014-01-29T09:43:15Z | |
dc.date.issued | 2007 | en_US |
dc.description.abstract | This paper introduces a novel technique for joint surface reconstruction and registration. Given a set of roughly aligned noisy point clouds, it outputs a noise-free and watertight solid model. The basic idea of the new technique is to reconstruct a prototype surface at increasing resolution levels, according to the registration accuracy obtained so far, and to register all parts with this surface. We derive a non-linear optimization problem from a Bayesian formulation of the joint estimation problem. The prototype surface is represented as a partition of unity implicit surface, which is constructed from piecewise quadratic functions defined on octree cells and blended together using B-spline basis functions, allowing the representation of objects with arbitrary topology with high accuracy. We apply the new technique to a set of standard data sets as well as especially challenging real-world cases. In practice, the novel prototype surface based joint reconstruction-registration algorithm avoids typical convergence problems in registering noisy range scans and substantially improves the accuracy of the final output. | en_US |
dc.description.seriesinformation | Geometry Processing | en_US |
dc.identifier.isbn | 978-3-905673-46-3 | en_US |
dc.identifier.issn | 1727-8384 | en_US |
dc.identifier.uri | https://doi.org/10.2312/SGP/SGP07/213-223 | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generation | en_US |
dc.title | Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype | en_US |
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