PointSFDA: Source-free Domain Adaptation for Point Cloud Completion

• URL: http://arxiv.org/abs/2503.15144v1
• Date: Wed, 19 Mar 2025 12:09:45 GMT
• Title: PointSFDA: Source-free Domain Adaptation for Point Cloud Completion
• Authors: Xing He, Zhe Zhu, Liangliang Nan, Honghua Chen, Jing Qin, Mingqiang Wei,
• Abstract summary: We propose an effective yet simple source-free domain adaptation framework for point cloud completion.<n>PointSFDA uses only a pretrained source model and unlabeled target data for adaptation.<n>Our method significantly improves the performance of state-of-the-art networks in cross-domain shape completion.
• Score: 27.48403130855686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional methods for point cloud completion, typically trained on synthetic datasets, face significant challenges when applied to out-of-distribution real-world scans. In this paper, we propose an effective yet simple source-free domain adaptation framework for point cloud completion, termed \textbf{PointSFDA}. Unlike unsupervised domain adaptation that reduces the domain gap by directly leveraging labeled source data, PointSFDA uses only a pretrained source model and unlabeled target data for adaptation, avoiding the need for inaccessible source data in practical scenarios. Being the first source-free domain adaptation architecture for point cloud completion, our method offers two core contributions. First, we introduce a coarse-to-fine distillation solution to explicitly transfer the global geometry knowledge learned from the source dataset. Second, as noise may be introduced due to domain gaps, we propose a self-supervised partial-mask consistency training strategy to learn local geometry information in the target domain. Extensive experiments have validated that our method significantly improves the performance of state-of-the-art networks in cross-domain shape completion. Our code is available at \emph{\textcolor{magenta}{https://github.com/Starak-x/PointSFDA}}.

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