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.

该论文介绍了一种动态方法，通过多次迭代点云数据的注册过程，识别匹配点聚类区域，从而删除噪点，并通过评估每个注册阶段的结果以提高网络效率。实验证明，该模型相较于其他方法在相似结果上显著减少时间消耗，室内数据集 (3DMatch) 速度提升超过 41%，室外数据集 (KITTI) 速度提升 33%，同时保持竞争性的注册召回需求。