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.

通过MIRETR，即Multi-Instance REgistration TRansformer方法，我们提出了一种粗到细的方法来提取具有实例感知的对应关系，通过联合学习实例感知超点特征和预测每个实例的掩码，从而最小化关注范围外的影响，提取可靠的超点对应关系，并根据实例掩码将超点对应关系扩展到实例候选项，最后通过高效的候选项选择和精调算法获取最终的配准结果。在三个公共基准测试中进行了广泛实验，结果表明我们的方法的有效性。特别是在具有挑战性的ROBI基准测试中，MIRETR在F1得分上超过了现有技术16.6个点。

学习鲁棒的多实例点云配准中的实例感知对应关系