Content-based Retrieval of 3D Models using Generative Modeling Techniques

Grabner, Harald; Ullrich, Torsten; Fellner, Dieter W.

Content-based Retrieval of 3D Models using Generative Modeling Techniques

Files

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paper1062_e-paper.pptx (791.21 KB)

Date

2014

Authors

Grabner, Harald
Ullrich, Torsten
Fellner, Dieter W.

Publisher

The Eurographics Association

Abstract

In this paper we present a novel 3D model retrieval approach based on generative modeling techniques. In our approach generative models are created by domain experts in order to describe 3D model classes. These generative models span a shape space, of which a number of training samples is taken at random. The samples are used to train content-based retrieval methods. With a trained classifier, techniques based on semantic enrichment can be used to index a repository. Furthermore, as our method uses solely generative 3D models in the training phase, it eliminates the cold start problem. We demonstrate the effectiveness of our method by testing it against the Princeton shape benchmark.

        @inproceedings{:10.2312/gch.20141317
,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage - Short Papers / Posters
},
editor = {Reinhard Klein and Pedro Santos
},
title = {{Content-based Retrieval of 3D Models using Generative Modeling Techniques
}},
author = {Grabner, Harald and 
Ullrich, Torsten and 
Fellner, Dieter W.
},
year = {2014
},
publisher = {The Eurographics Association
},
ISBN = {978-3-905674-75-0
},
DOI = {/10.2312/gch.20141317
}
}