Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://github.com/mordecaimalignatius/GAFAR/.

翻译：点云的刚性配准是计算机视觉中的一个基本问题，广泛应用于三维场景重建、几何捕捉和机器人等领域。若存在合适的初始配准，传统方法如ICP及其众多变体可以提供充分的解决方案。然而，在缺乏合适初始配准、存在高异常值率或点云重叠度较低的情况下，刚性配准任务仍面临巨大挑战。深度学习方法在计算机视觉领域的兴起为这一研究带来了新动力，因为其能够学习具有表达力的特征表示，并提供一次性的参数估计，从而避免传统鲁棒方法中耗时的迭代过程。然而，点云本身具有的旋转和置换不变性给深度学习带来了独特挑战，导致网络对异常值及训练过程中未出现的三维扫描特征敏感，进而引发性能下降和泛化能力不足。在本文中，我们提出了一种新颖的快速轻量级网络架构，利用注意力机制在推理时增强点描述符，使其能针对所处理特定点云的配准任务进行优化。通过构建点云内部及点云之间的全连接图，该网络能够推理各点对配准任务的重要性和可靠性，从而使其对异常值、低重叠度及未见数据具有鲁棒性。我们在不同的配准与泛化任务上测试了算法的性能，并提供了运行时间和资源消耗的相关信息。代码与预训练权重可在https://github.com/mordecaimalignatius/GAFAR/获取。