Active exploration of large 3D model repositories

Gao, Lin, Cao, Yan-Pei, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Huang, Hao-Zhi, Kobbelt, Leif and Hu, Shi-Min ORCID: https://orcid.org/0000-0001-7507-6542 2015. Active exploration of large 3D model repositories. IEEE Transactions on Visualization and Computer Graphics , pp. 1390-1402. 10.1109/TVCG.2014.2369039

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Abstract

With broader availability of large-scale 3D model repositories, the need for efficient and effective exploration becomes more and more urgent. Existing model retrieval techniques do not scale well with the size of the database since often a large number of very similar objects are returned for a query, and the possibilities to refine the search are quite limited. We propose an interactive approach where the user feeds an active learning procedure by labeling either entire models or parts of them as “like” or “dislike” such that the system can automatically update an active set of recommended models. To provide an intuitive user interface, candidate models are presented based on their estimated relevance for the current query. From the methodological point of view, our main contribution is to exploit not only the similarity between a query and the database models but also the similarities among the database models themselves. We achieve this by an offline pre-processing stage, where global and local shape descriptors are computed for each model and a sparse distance metric is derived that can be evaluated efficiently even for very large databases. We demonstrate the effectiveness of our method by interactively exploring a repository containing over 100K models.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/1077-2626/ (accessed 10.12.14).
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1077-2626
Date of First Compliant Deposit:	30 March 2016
Last Modified:	23 Nov 2024 08:30
URI:	https://orca.cardiff.ac.uk/id/eprint/68266

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