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Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views

View-based deep learning models have shown the capability to learn 3D shape descriptors with superior performance on 3D shape recognition, classification, and retrieval. Most popular techniques often leverage the class label to train deep neural networks under supervision to learn to extract 3D deep representation by aggregating information from a static and pre-selected set of different views used for all shapes. Those approaches, however, often face challenges posed by the requirement of a large amount of annotated training data and the lack of a mechanism for the adaptive selection of shape-instance-dependent views towards the learning of more informative 3D shape representation. This paper addresses those two challenging issues by introducing the concept of adversarial views and developing a new mechanism to generate views for adversarial training of a self-supervised contrastive model for 3D shape descriptor, denoted as CoLAV. In particular, compared to the recent advances in multi-view approaches, our proposed CoLAV gains advantages by leveraging the contrastive learning techniques for self-supervised learning of 3D shape representations without the need for labeled data. In addition, CoLAV introduces a novel mechanism for the dynamic generation of shape-instance-dependent adversarial views as positive pairs to adversarially train robust contrastive learning models towards the learning of more informative 3D shape representation. Comprehensive experimental results on 3D shape classification demonstrate that the 3D shape descriptor learned by CoLAV exhibits superior performance for 3D shape recognition over other state-of-the-art techniques, even though CoLAV is completely self-trained with unlabeled 3D datasets (e.g., ModelNet40).

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