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.

本文提出了一种新的多视点点云配准方法，使用神经网络评估扫描对之间的重叠，构建稀疏的姿态图，并在 IRLS 方案中设计了一种新的历史加权函数以提高对异常边缘的强鲁棒性，实验结果表明，相对于现有方法，我们的方法在 3DMatch 数据集上实现了 11% 的更高配准召回率，在 ScanNet 数据集上实现了约 13% 的更低配准误差，降低了约 70% 的必要的成对注册。