Dr. Charu Sharma  and her students published the following papers at Proceedings of the  IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2024) held at Waikoloa, Hawaii from 4 to 8 January. 

• Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation – Charu Sharma; Katageri Siddharth; Arkadipta De, Fujitsu Research India; Chaitanya Devaguptapu, Fujitsu Research India; V S S V Prasad, Fujitsu Research India and Manohar Kaul, Fujitsu Research India.

Research work as explained by the authors:

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 Point DA-10 and GraspNetPC-10 and show that our method achieves state-of-the-art performance on GraspNet PC-10 (with ≈ 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

Full paper: https://openaccess.thecvf.com/content/WACV2024/papers/Katageri_Synergizing_Contrastive_Learning_and_Optimal_Transport_for_3D_Point_Cloud_WACV_2024_paper.pdf

• Metric Learning for 3D Point Clouds Using Optimal Transport – Charu Sharma; Siddharth Katageri and Srinjay Sarkar. 

Research work as explained by the authors:

Learning embeddings of any data largely depends on the ability of the target space to capture semantic relations. The widely used Euclidean space, where embeddings are represented as point vectors, is known to be lacking in its potential to exploit complex structures and relations. Contrary to standard Euclidean embeddings, in this work, we embed point clouds as discrete probability distributions in Wasserstein space. We build a contrastive learning setup to learn Wasserstein embeddings that can be used as a pre-training method with or without supervision towards any downstream task. We show that the features captured by Wasserstein embeddings are better in preserving the point cloud geometry, including both global and local information, thus resulting in improved quality embeddings. We perform exhaustive experiments and demonstrate the effectiveness of our method for point cloud classification, transfer learning, segmentation, and interpolation tasks over multiple datasets including synthetic and real-world objects. We also compare against recent methods that use Wasserstein space and show that our method outperforms them in all downstream tasks. Additionally, our study reveals a promising interpretation of capturing critical points of point clouds that makes our proposed method self-explainable.

Full paper: https://openaccess.thecvf.com/content/WACV2024W/Pretrain/papers/Katageri_Metric_Learning_for_3D_Point_Clouds_Using_Optimal_Transport_WACVW_2024_paper.pdf

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Conference page: https://wacv2024.thecvf.com/