DeepAI AI Chat
Log In Sign Up

Self-Distillation for Unsupervised 3D Domain Adaptation

by   Adriano Cardace, et al.
University of Bologna

Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead, breaks this assumption and tries to solve the task on an unlabeled target domain, leveraging only on a supervised source domain. For point cloud classification, recent UDA methods try to align features across domains via auxiliary tasks such as point cloud reconstruction, which however do not optimize the discriminative power in the target domain in feature space. In contrast, in this work, we focus on obtaining a discriminative feature space for the target domain enforcing consistency between a point cloud and its augmented version. We then propose a novel iterative self-training methodology that exploits Graph Neural Networks in the UDA context to refine pseudo-labels. We perform extensive experiments and set the new state-of-the-art in standard UDA benchmarks for point cloud classification. Finally, we show how our approach can be extended to more complex tasks such as part segmentation.


page 1

page 2

page 3

page 4


RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

Unsupervised Domain Adaptation (UDA) for point cloud classification is a...

A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds

Deep neural networks have achieved promising performance in supervised p...

Self-Ensemling for 3D Point Cloud Domain Adaption

Recently 3D point cloud learning has been a hot topic in computer vision...

Classifying In-Place Gestures with End-to-End Point Cloud Learning

Walking in place for moving through virtual environments has attracted n...

SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification

Although Domain Generalization (DG) problem has been fast-growing in the...

PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

Domain Adaptation (DA) approaches achieved significant improvements in a...

Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation

Recently, the fundamental problem of unsupervised domain adaptation (UDA...