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

• URL: http://arxiv.org/abs/2304.00467v1
• Date: Sun, 2 Apr 2023 06:43:40 GMT
• Title: Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
• Authors: Haiping Wang, Yuan Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping Wang, Bisheng Yang
• Abstract summary: We present a new method for the multiview registration of point cloud. Our method achieves 11% higher registration recall on the 3DMatch dataset and 13% lower registration errors on the ScanNet dataset.
• Score: 63.95845583460312
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ~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.

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