Scalable Visual Exploration of 3D Shape Databases via Feature Synthesis and Selection

Xingyu Chen, Guangping Zeng, Jiri Kosinka, Alexandru C. Telea*

*Bijbehorende auteur voor dit werk

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We present a set of techniques to address the problem of scalable creation of visual overview representations of large 3D shape databases based on dimensionality reduction of feature vectors extracted from shape descriptions. We address the problem of feature extraction by exploring both combinations of hand-engineered geometric features and using the latent feature vectors generated by a deep learning classification method, and discuss the comparative advantages of both approaches. Separately, we address the problem of generating insightful 2D projections of these feature vectors that are able to separate well different groups of similar shapes by two approaches. First, we create quality projections by both automatic search in the space of feature combinations and, alternatively, by leveraging human insight to improve projections by iterative feature selection. Secondly, we use deep learning to automatically construct projections from the extracted features. We show that our three variations of deep learning, which jointly treat feature extraction, selection, and projection, allow efficient creation of high-quality visual overviews of large shape collections, require minimal user intervention, and are easy to implement. We demonstrate our approach on several real-world 3D shape databases.
Originele taal-2English
TitelComputer Vision, Imaging and Computer Graphics Theory and Applications
Subtitel15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27–29, 2020, Revised Selected Papers
RedacteurenKadi Bouatouch, A. Augusto de Sousa, Manuela Chessa, Alexis Paljic, Andreas Kerren, Christophe Hurter, Giovanni Maria Farinella, Petia Radeva, Jose Braz
Aantal pagina's30
ISBN van elektronische versie978-3-030-94893-1
ISBN van geprinte versie978-3-030-94892-4
StatusPublished - jan.-2022

Publicatie series

NaamCommunications in Computer and Information Science
ISSN van geprinte versie1865-0929
ISSN van elektronische versie1865-0937

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