DWG-Reg: Deep Weight Global Registration

Biao Li, Qixing Xie, Shaoyi Du, Wenting Cui, Runzhao Yao, Yang Yang, Jing Yang, Lin Wang

Abstract: In this paper, we propose a deep weight global registration (DWG-Reg) algorithm for poor initialization and partially overlapping point clouds registration problem. Our DWG-Reg is based on three modules: a bidirectional nearest search strategy for correspondence, a convolutional network for correspondence confidence prediction which consists of Hybird Distance Generator, optimal annealing Parameter Prediction network and a robust kernel function, a weighted optimizer algorithm for closed-form pose estimation. Experimental results show that our DWG-Reg achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code will open at https://github.com/BiaoBiaoLi/DWG-Reg.