Grabner, Harald ; Ullrich, Torsten ; Fellner, Dieter W. (2014)
Content-based Retrieval of 3D Models using Generative Modeling Techniques.
GCH 2014. Short Papers - Posters.
doi: 10.2312/gch.20141317
Conference or Workshop Item, Bibliographie
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2014 |
| Creators: | Grabner, Harald ; Ullrich, Torsten ; Fellner, Dieter W. |
| Type of entry: | Bibliographie |
| Title: | Content-based Retrieval of 3D Models using Generative Modeling Techniques |
| Language: | English |
| Date: | 2014 |
| Publisher: | Eurographics Association, Goslar |
| Event Title: | GCH 2014. Short Papers - Posters |
| DOI: | 10.2312/gch.20141317 |
| 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. |
| Uncontrolled Keywords: | Business Field: Digital society, Research Area: Modeling (MOD), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Computer graphics, Information systems, Content based retrieval, Knowledge representation, Scene analysis, Object recognition, Generative modeling |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 12 Nov 2018 11:16 |
| Last Modified: | 04 Feb 2022 12:39 |
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