A global geometric framework for 3D shape retrieval using deep learning

摘要

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. (C) 2018 Elsevier Ltd. All rights reserved.