Abstract
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 $\sim13$\% lower registration errors on the ScanNet dataset while reducing $\sim70$\% required pairwise registrations. Comprehensive ablation studies are conducted to demonstrate the effectiveness of our designs.
Fig.1 Pipeline of SGHR. Given $N$ unaligned partial scans (1), our target is to register all these scans into a completed point cloud (4). SGHR has two contributions. (2) We learn a global feature vector to initialize a sparse pose graph which contains much less outliers and reduces the required number of pairwise registrations. (3) We propose a novel IRLS scheme. In our IRLS scheme, we initialize weights from both global features and pairwise registrations. Then, we design a history reweighting function to iteratively refine poses, which improves the robustness to outliers.
