  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.
