We present Point-TTA, a novel test-time adaptation framework for point cloud
registration (PCR) that improves the generalization and the performance of
registration models. While learning-based approaches have achieved impressive
progress, generalization to unknown testing environments remains a major
challenge due to the variations in 3D scans. Existing methods typically train a
generic model and the same trained model is applied on each instance during
testing. This could be sub-optimal since it is difficult for the same model to
handle all the variations during testing. In this paper, we propose a test-time
adaptation approach for PCR. Our model can adapt to unseen distributions at
test-time without requiring any prior knowledge of the test data. Concretely,
we design three self-supervised auxiliary tasks that are optimized jointly with
the primary PCR task. Given a test instance, we adapt our model using these
auxiliary tasks and the updated model is used to perform the inference. During
training, our model is trained using a meta-auxiliary learning approach, such
that the adapted model via auxiliary tasks improves the accuracy of the primary
task. Experimental results demonstrate the effectiveness of our approach in
improving generalization of point cloud registration and outperforming other
state-of-the-art approaches.

我们提出了一种新颖的测试时间自适应框架 (Point-TTA) 用于点云配准 (PCR)，它提高了配准模型的泛化性和性能。我们的模型可以在测试时间适应未知分布，不需要对测试数据有任何先验知识，通过优化主要的 PCR 任务和三个自监督辅助任务来训练模型，实验证明了我们的方法在提高点云配准的泛化性上的有效性并且胜过其他最先进的方法。