A global geometric framework for 3D shape retrieval using deep learning. (April 2019)

• Record Type: Journal Article
• Authors: Luciano, Lorenzo; Ben Hamza, A.
• Is Part Of: Computers & graphics. Volume 79(2019)
• Page Start: 14
• Page End: 23
• Publication Date: 2019-04
• Subjects: Shape retrieval -- Deep learning -- Geodesic moments; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2018.12.003
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: We present a geometric framework for 3D shape retrieval using geodesic moments. We propose an unsupervised approach for learning shape descriptors using sparse autoencoders. We demonstrate the superior performance of our approach on standard shape benchmarks. Graphical abstract: Abstract: Shape representations provide compact, parsimonious shape descriptions that are often used in object recognition and retrieval tasks. 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 numerous computer vision and geometry processing applications. In this paper, we present a deep learning framework for unsupervised 3D shape retrieval with geodesic moments. The proposed method learns deep shape representations using stacked sparse autoencoders in an unsupervised manner. Such discriminative shape descriptors can then be used to compute 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 four standard 3D shape benchmarks demonstrate the competitive performance of our approach, showing that it leads to improved retrieval results in comparison with state-of-the-art techniques.