  Multiview point cloud registration serves as a cornerstone of various
computer vision tasks. Previous approaches typically adhere to a global
paradigm, where a pose graph is initially constructed followed by motion
synchronization to determine the absolute pose. However, this separated
approach may not fully leverage the characteristics of multiview registration
and might struggle with low-overlap scenarios. In this paper, we propose an
incremental multiview point cloud registration method that progressively
registers all scans to a growing meta-shape. To determine the incremental
ordering, we employ a two-stage coarse-to-fine strategy for point cloud
candidate retrieval. The first stage involves the coarse selection of scans
based on neighbor fusion-enhanced global aggregation features, while the second
stage further reranks candidates through geometric-based matching.
Additionally, we apply a transformation averaging technique to mitigate
accumulated errors during the registration process. Finally, we utilize a
Reservoir sampling-based technique to address density variance issues while
reducing computational load. Comprehensive experimental results across various
benchmarks validate the effectiveness and generalization of our approach.
