Deep learning is a rapidly growing discipline that models high-level features in data as multilayered neural networks. In this paper, we propose a deep learning approach for 3D shape retrieval using a multi-level feature learning methodology. We first extract low-level features or local descriptors from a 3D shape using spectral graph wavelets. Then, we construct mid-level features from these local descriptors via the bag-of-features paradigm by employing locality-constrained linear coding as a feature coding method, together with the biharmonic distance as a measure of the spatial relationship between each pair of bag-of-feature descriptors. Finally, high-level shape features are learned via a deep auto-encoder, resulting in a deep shape-aware descriptor that is compact, geometrically informative and efficient to compute. The proposed 3D shape retrieval approach is evaluated on SHREC-2014 and SHREC-2015 datasets through extensive experiments, and the results show compelling superiority of our approach over the state-of-the-art methods.
['Hamed Ghodrati', 'A. Ben Hamza']
Deep shape-aware descriptor for nonrigid 3D object retrieval