  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.
