  Point cloud registration is a key task in many computational fields. Previous
correspondence matching based methods require the inputs to have distinctive
geometric structures to fit a 3D rigid transformation according to point-wise
sparse feature matches. However, the accuracy of transformation heavily relies
on the quality of extracted features, which are prone to errors with respect to
partiality and noise. In addition, they can not utilize the geometric knowledge
of all the overlapping regions. On the other hand, previous global feature
based approaches can utilize the entire point cloud for the registration,
however they ignore the negative effect of non-overlapping points when
aggregating global features. In this paper, we present OMNet, a global feature
based iterative network for partial-to-partial point cloud registration. We
learn overlapping masks to reject non-overlapping regions, which converts the
partial-to-partial registration to the registration of the same shape.
Moreover, the previously used data is sampled only once from the CAD models for
each object, resulting in the same point clouds for the source and reference.
We propose a more practical manner of data generation where a CAD model is
sampled twice for the source and reference, avoiding the previously prevalent
over-fitting issue. Experimental results show that our method achieves
state-of-the-art performance compared to traditional and deep learning based
methods. Code is available at https://github.com/megvii-research/OMNet.
