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。

可靠姿态图初始化的多视角点云强匹配及历史重加权

Robust Multiview Point Cloud Registration with Reliable Pose Graph  Initialization and History Reweighting

We provide the first global model recovery results for the IRLS (iteratively
reweighted least squares) heuristic for robust regression problems. IRLS is
known to offer excellent performance, despite bad initializations and data
corruption, for several parameter estimation problems. Existing analyses of
IRLS frequently require careful initialization, thus offering only local
convergence guarantees. We remedy this by proposing augmentations to the basic
IRLS routine that not only offer guaranteed global recovery, but in practice
also outperform state-of-the-art algorithms for robust regression. Our routines
are more immune to hyperparameter misspecification in basic regression tasks,
as well as applied tasks such as linear-armed bandit problems. Our theoretical
analyses rely on a novel extension of the notions of strong convexity and
smoothness to weighted strong convexity and smoothness, and establishing that
sub-Gaussian designs offer bounded weighted condition numbers. These notions
may be useful in analyzing other algorithms as well.

该研究提出了对 IRLS 鲁棒回归问题的全球模型恢复结果，建议加强基本 IRLS 例程，提供全球恢复的保证，可更好地抵御基本回归任务和应用任务的超参数错误，使用加权强凸和平稳性的新概念来理论分析。