Learning Instance-Aware Correspondences for Robust Multi-Instance Point
  Cloud Registration in Cluttered Scenes
Zhiyuan Yu Zheng Qin Lintao Zheng Kai Xu
Multi-instance point cloud registration estimates the poses of multiple
instances of a model point cloud in a scene point cloud. Extracting accurate
point correspondence is to the center of the problem. Existing approaches
usually treat the scene point cloud as a whole, overlooking the separation of
instances. Therefore, point features could be easily polluted by other points
from the background or different instances, leading to inaccurate
correspondences oblivious to separate instances, especially in cluttered
scenes. In this work, we propose MIRETR, Multi-Instance REgistration
TRansformer, a coarse-to-fine approach to the extraction of instance-aware
correspondences. At the coarse level, it jointly learns instance-aware
superpoint features and predicts per-instance masks. With instance masks, the
influence from outside of the instance being concerned is minimized, such that
highly reliable superpoint correspondences can be extracted. The superpoint
correspondences are then extended to instance candidates at the fine level
according to the instance masks. At last, an efficient candidate selection and
refinement algorithm is devised to obtain the final registrations. Extensive
experiments on three public benchmarks demonstrate the efficacy of our
approach. In particular, MIRETR outperforms the state of the arts by 16.6
points on F1 score on the challenging ROBI benchmark. Code and models are
available at https://github.com/zhiyuanYU134/MIRETR.