Geodesic-based 3D Shape Retrieval Using Sparse Autoencoders

Luciano, Lorenzo; Hamza, Abdessamad Ben

Geodesic-based 3D Shape Retrieval Using Sparse Autoencoders

Files

021-028.pdf (2.42 MB)

Date

2018

Authors

Luciano, Lorenzo
Hamza, Abdessamad Ben

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.

        @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
}
}