Bi-Adversarial Discrepancy Minimization for Unsupervised Domain Adaptation on 3D Point Cloud

Domain Adaptation (DA) has brought significant progress to a wide range of computer vision tasks. However, there are few methods that explores domain adaptation on 3D point cloud data, which plays crucial role in 3D recognition tasks (i.e., autonomous driving and robotics). In a nutshell, the unique challenges of point cloud data consist of abundant spatial global and local geometric information, complicated data distribution, and more ambiguous margins. In this paper, we introduce a novel Bi-Adversarial Discrepancy Minimization network (BADM), which aligns the cross-domain point cloud data by adopting two adversarial learning units with respect to global representations. Based on PointNet, we also utilize local alignment to reduce the discrepancy of local structures. In addition, our BADM method helps define more accurate margin and decision bounds. For this 3D point cloud DA scenario, we use PointDA-10 as the benchmark, which is extracted from ModelNet, ShapeNet, and ScanNet for cross-domain 3D object classification (10 mutual classes). Extensive experiments demonstrate that our approach achieves state-of-the-art performance, outperforming other 3D DA approaches.