Wasserstein distance feature alignment learning for 2D image-based 3D model retrieval

Abstract 2D image-based 3D model retrieval has become a hotspot topic in recent years. However, the current existing methods are limited by two aspects. Firstly, they are mostly based on the supervised learning, which limits their application because of the high time and cost consuming of manual annotation. Secondly, the mainstream methods narrow the discrepancy between 2D and 3D domains mainly by the image-level alignment, which may bring the additional noise during the image transformation and influence cross-domain effect. Consequently, we propose a Wasserstein distance feature alignment learning (WDFAL) for this retrieval task. First of all, we describe 3D models through a series of virtual views and use CNNs to extract features. Secondly, we design a domain critic network based on the Wasserstein distance to narrow the discrepancy between two domains. Compared to the image-level alignment, we reduce the domain gap by the feature-level distribution alignment to avoid introducing additional noise. Finally, we extract the visual features from 2D and 3D domains, and calculate their similarity by utilizing Euclidean distance. The extensive experiments can validate the superiority of the WDFAL method.