  Point cloud-based medical registration promises increased computational
efficiency, robustness to intensity shifts, and anonymity preservation but is
limited by the inefficacy of unsupervised learning with similarity metrics.
Supervised training on synthetic deformations is an alternative but, in turn,
suffers from the domain gap to the real domain. In this work, we aim to tackle
this gap through domain adaptation. Self-training with the Mean Teacher is an
established approach to this problem but is impaired by the inherent noise of
the pseudo labels from the teacher. As a remedy, we present a denoised
teacher-student paradigm for point cloud registration, comprising two
complementary denoising strategies. First, we propose to filter pseudo labels
based on the Chamfer distances of teacher and student registrations, thus
preventing detrimental supervision by the teacher. Second, we make the teacher
dynamically synthesize novel training pairs with noise-free labels by warping
its moving inputs with the predicted deformations. Evaluation is performed for
inhale-to-exhale registration of lung vessel trees on the public PVT dataset
under two domain shifts. Our method surpasses the baseline Mean Teacher by
13.5/62.8%, consistently outperforms diverse competitors, and sets a new
state-of-the-art accuracy (TRE=2.31mm). Code is available at
https://github.com/multimodallearning/denoised_mt_pcd_reg.
