Geodesic-based 3D Shape Retrieval Using Sparse Autoencoders
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In light of the increased processing power of graphics cards and the availability of large-scale datasets, deep neural networks have shown a remarkable performance in various visual computing applications. In this paper, we propose a geometric framework for unsupervised 3D shape retrieval using geodesic moments and stacked sparse autoencoders. The key idea is to learn deep shape representations in an unsupervised manner. Such discriminative shape descriptors can then be used to compute the pairwise dissimilarities between shapes in a dataset, and to find the retrieved set of the most relevant shapes to a given shape query. Experimental evaluation on three standard 3D shape benchmarks demonstrate the competitive performance of our approach in comparison with state-of-the-art techniques.
Description
@inproceedings{10.2312:3dor.20181049,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco},
title = {{Geodesic-based 3D Shape Retrieval Using Sparse Autoencoders}},
author = {Luciano, Lorenzo and Hamza, Abdessamad Ben},
year = {2018},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-053-6},
DOI = {10.2312/3dor.20181049}
}