Nonrigid 3D shape retrieval using deep auto-encoders

作者：Ghodrati Hamed; Ben Hamza A^*

来源：Applied Intelligence, 2017, 47(1): 44-61.

DOI：10.1007/s10489-016-0880-1

• Shape retrieval
• Deep learning
• Spectral graph wavelets
• Biharmonic distance
• Intrinsic partition

The soaring popularity of deep learning in a wide variety of fields ranging from computer vision and speech recognition to self-driving vehicles has sparked a flurry of research interest from both academia and industry. In this paper, we propose a deep learning approach to 3D shape retrieval using a multi-level feature learning paradigm. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Then, mid-level features are generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-of-feature descriptors. Finally, high-level shape features are learned by applying a deep auto-encoder on mid-level features. Extensive experiments on SHREC-2014 and SHREC-2015 datasets demonstrate the much better performance of the proposed framework in comparison with state-of-the-art methods.

• 出版日期2017-7