Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. In this work, we propose the first iterative framework called instance-by-instance (IBI) for multi-instance 3D registration (MI-3DReg). It successively registers all instances in a given scenario, starting from the easiest and progressing to more challenging ones. Throughout the iterative process, outliers are eliminated continuously, leading to an increasing inlier rate for the remaining and more challenging instances. Under the IBI framework, we further propose a sparse-to-dense-correspondence-based multi-instance registration method (IBI-S2DC) to achieve robust MI-3DReg. Experiments on the synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance of IBI-S2DC, e.g., our MHF1 is 12.02%/12.35% higher than the existing state-of-the-art method ECC on the synthetic/real datasets.

翻译：多实例配准是计算机视觉与机器人领域中的一项挑战性问题，其目标是将同一物体的多个实例统一配准至标准坐标系。本文首次提出名为“逐实例迭代”（IBI）的迭代框架，用于解决多实例三维配准（MI-3DReg）问题。该框架从最易处理的实例开始，逐步推进至更具挑战性的实例，实现对给定场景中所有实例的连续配准。在迭代过程中，异常值被持续剔除，使得剩余且更具挑战性实例的内点率逐步提升。基于IBI框架，我们进一步提出基于稀疏到稠密对应的多实例配准方法（IBI-S2DC），以实现鲁棒的MI-3DReg。在合成数据集与真实数据集上的实验验证了IBI的有效性，并表明IBI-S2DC达到了新的最优性能——例如，在合成/真实数据集上，我们的MHF1指标分别比现有最优方法ECC高出12.02%/12.35%。