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.

翻译：我们提出Point-TTA，一种新颖的测试时自适应框架，用于点云配准（PCR），旨在提升配准模型的泛化能力与性能。尽管基于学习的方法已取得显著进展，但由于三维扫描数据的多样性，模型在未知测试环境中的泛化仍面临重大挑战。现有方法通常训练通用模型，并在测试阶段对每个实例应用同一训练模型。然而，由于同一模型难以应对测试中的所有变化，这可能导致次优结果。本文提出一种面向PCR的测试时自适应方法，该方法无需任何测试数据先验知识，即可在测试阶段适应未见过的数据分布。具体而言，我们设计了三个自监督辅助任务，与主PCR任务联合优化。给定测试实例后，利用辅助任务对模型进行自适应，并使用更新后的模型执行推理。在训练阶段，采用元辅助学习方式训练模型，使得通过辅助任务自适应后的模型能够提升主任务的准确性。实验结果证明了该方法在提升点云配准泛化能力方面的有效性，并优于其他前沿方法。