  Recently, the fundamental problem of unsupervised domain adaptation (UDA) on
3D point clouds has been motivated by a wide variety of applications in
robotics, virtual reality, and scene understanding, to name a few. The point
cloud data acquisition procedures manifest themselves as significant domain
discrepancies and geometric variations among both similar and dissimilar
classes. The standard domain adaptation methods developed for images do not
directly translate to point cloud data because of their complex geometric
nature. To address this challenge, we leverage the idea of multimodality and
alignment between distributions. We propose a new UDA architecture for point
cloud classification that benefits from multimodal contrastive learning to get
better class separation in both domains individually. Further, the use of
optimal transport (OT) aims at learning source and target data distributions
jointly to reduce the cross-domain shift and provide a better alignment. We
conduct a comprehensive empirical study on PointDA-10 and GraspNetPC-10 and
show that our method achieves state-of-the-art performance on GraspNetPC-10
(with approx 4-12% margin) and best average performance on PointDA-10. Our
ablation studies and decision boundary analysis also validate the significance
of our contrastive learning module and OT alignment.
