Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype

Huang, Qi-Xing; Adams, Bart; Wand, Michael

Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype

Files

213-223.pdf (391.74 KB)

Date

2007

Authors

Huang, Qi-Xing
Adams, Bart
Wand, Michael

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.

        @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
}
}