  In this paper, we present a new method for the multiview registration of
point cloud. Previous multiview registration methods rely on exhaustive
pairwise registration to construct a densely-connected pose graph and apply
Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the
scan poses. However, constructing a densely-connected graph is time-consuming
and contains lots of outlier edges, which makes the subsequent IRLS struggle to
find correct poses. To address the above problems, we first propose to use a
neural network to estimate the overlap between scan pairs, which enables us to
construct a sparse but reliable pose graph. Then, we design a novel history
reweighting function in the IRLS scheme, which has strong robustness to outlier
edges on the graph. In comparison with existing multiview registration methods,
our method achieves 11% higher registration recall on the 3DMatch dataset and
~13% lower registration errors on the ScanNet dataset while reducing ~70%
required pairwise registrations. Comprehensive ablation studies are conducted
to demonstrate the effectiveness of our designs.
