Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype

dc.contributor.authorHuang, Qi-Xingen_US
dc.contributor.authorAdams, Barten_US
dc.contributor.authorWand, Michaelen_US
dc.contributor.editorAlexander Belyaev and Michael Garlanden_US
dc.date.accessioned2014-01-29T09:43:15Z
dc.date.available2014-01-29T09:43:15Z
dc.date.issued2007en_US
dc.description.abstractThis 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.seriesinformationGeometry Processingen_US
dc.identifier.isbn978-3-905673-46-3en_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttps://doi.org/10.2312/SGP/SGP07/213-223en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Line and Curve Generationen_US
dc.titleBayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototypeen_US
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