Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype

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Date
2007
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description

        
@inproceedings{
:10.2312/SGP/SGP07/213-223
, booktitle = {
Geometry Processing
}, editor = {
Alexander Belyaev and Michael Garland
}, title = {{
Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype
}}, author = {
Huang, Qi-Xing
and
Adams, Bart
and
Wand, Michael
}, year = {
2007
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-8384
}, ISBN = {
978-3-905673-46-3
}, DOI = {
/10.2312/SGP/SGP07/213-223
} }
Citation