Surface Reconstruction using Local Shape Priors

Gal, Ran; Shamir, Ariel; Hassner, Tal; Pauly, Mark; Or, Daniel Cohen

Surface Reconstruction using Local Shape Priors

Files

253-262.pdf (417.8 KB)

Date

2007

Authors

Gal, Ran
Shamir, Ariel
Hassner, Tal
Pauly, Mark
Or, Daniel Cohen

Publisher

The Eurographics Association

Abstract

We present an example-based surface reconstruction method for scanned point sets. Our approach uses a database of local shape priors built from a set of given context models that are chosen specifically to match a specific scan. Local neighborhoods of the input scan are matched with enriched patches of these models at multiple scales. Hence, instead of using a single prior for reconstruction, our method allows specific regions in the scan to match the most relevant prior that fits best. Such high confidence matches carry relevant information from the prior models to the scan, including normal data and feature classification, and are used to augment the input point-set. This allows to resolve many ambiguities and difficulties that come up during reconstruction, e.g., distinguishing between signal and noise or between gaps in the data and boundaries of the model. We demonstrate how our algorithm, given suitable prior models, successfully handles noisy and under-sampled point sets, faithfully reconstructing smooth regions as well as sharp features.

        @inproceedings{:10.2312/SGP/SGP07/253-262
,
booktitle = {Geometry Processing
},
editor = {Alexander Belyaev and Michael Garland
},
title = {{Surface Reconstruction using Local Shape Priors
}},
author = {Gal, Ran and 
Shamir, Ariel and 
Hassner, Tal and 
Pauly, Mark and 
Or, Daniel Cohen
},
year = {2007
},
publisher = {The Eurographics Association
},
ISSN = {1727-8384
},
ISBN = {978-3-905673-46-3
},
DOI = {/10.2312/SGP/SGP07/253-262
}
}