PointADAM: Unsupervised Adversarial Domain Adaptation on Point Clouds With Metric Learning via Compact Feature Representation
Jiajia Lu,
Wun-She Yap,
Kok-Chin Khor
Abstract:Domain adaptation can mitigate the problem of limited labels in deep learning training. Nevertheless, extending the 2D domain adaptation method directly to 3D encounters challenges unique to point clouds, frequently resulting in inadequate feature alignment and a lack of discriminative features for decision boundaries. In light of this, we propose an unsupervised adversarial domain adaptation with metric learning (PointADAM) via compact feature representation. PointADAM is a two-stage architecture. In the firs… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.