For the point cloud registration task, a significant challenge arises from non-overlapping points that consume extensive computational resources while negatively affecting registration accuracy. In this paper, we introduce a dynamic approach, widely utilized to improve network efficiency in computer vision tasks, to the point cloud registration task. We employ an iterative registration process on point cloud data multiple times to identify regions where matching points cluster, ultimately enabling us to remove noisy points. Specifically, we begin with deep global sampling to perform coarse global registration. Subsequently, we employ the proposed refined node proposal module to further narrow down the registration region and perform local registration. Furthermore, we utilize a spatial consistency-based classifier to evaluate the results of each registration stage. The model terminates once it reaches sufficient confidence, avoiding unnecessary computations. Extended experiments demonstrate that our model significantly reduces time consumption compared to other methods with similar results, achieving a speed improvement of over 41% on indoor dataset (3DMatch) and 33% on outdoor datasets (KITTI) while maintaining competitive registration recall requirements.

翻译：在点云配准任务中，一个主要挑战来自非重叠点，这些点消耗大量计算资源并影响配准精度。本文提出一种动态方法——该方法已在计算机视觉任务中被广泛用于提升网络效率——将其引入点云配准任务。我们通过对点云数据多次执行迭代配准流程，识别匹配点聚集的区域，最终实现噪声点移除。具体而言，首先通过深度全局采样执行粗粒度全局配准；随后采用本文提出的精细化节点提议模块进一步缩小配准区域并执行局部配准；此外，利用基于空间一致性的分类器评估每个配准阶段的结果。当模型达到足够置信度时即终止，避免不必要的计算。大量实验表明，与取得相似结果的其他方法相比，本模型显著降低了时间消耗：在室内数据集（3DMatch）上速度提升超过41%，在室外数据集（KITTI）上提升33%，同时保持具有竞争力的配准召回率。