PointSFDA: Source-Free Domain Adaptation for Point Cloud Completion

Abstract

Point cloud completion is critical for autonomous driving and robotic perception, yet deep learning models often experience severe performance degradation under the domain gap between synthetic training and real-world data. While unsupervised domain adaptation (UDA) has been explored to mitigate this issue, its reliance on access to source datasets limits practical applicability, as source data are often proprietary or restricted.We pioneer source-free domain adaptation (SFDA) for point cloud completion, which adapts a pre-trained source model to an unlabeled target domain without requiring source data access. To this end, we propose PointSFDA, a framework that combines global knowledge transfer with target-specific local adaptation. Specifically, we design (i) a Coarse-to-Fine Point Cloud Distillation module to extract domain-invariant global geometric priors from the source model, and (ii) a Partial-Mask Consistency Training strategy to enforce prediction consistency across masking augmentations, enabling self-supervised learning of local target-domain geometry. Experiments on real-world datasets (KITTI, ScanNet) and synthetic benchmarks (ModelNet40, 3D-FUTURE) demonstrate that PointSFDA achieves significant improvements over state-of-the-art methods in cross-domain shape completion, establishing a practical and scalable solution for robotics applications. Our code is available at https://github.com/Starak-x/PointSFDA.