仮想対応点を用いたエンドツーエンド3D点群位置合わせ学習
End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences
LPD-Netとself/cross-attentionで2つの点群の特徴を抽出し、ソフトポインタで仮想対応点を生成してSVDで位置合わせを解くエンドツーエンド手法を提案した論文。
著者: Zhijian Qiao, Huanshu Wei, Zhe Liu, Chuanzhe Suo, Hesheng Wang
分類: cs.CV, cs.RO
原文アブストラクト
3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem. Firstly, the revised LPD-Net is introduced to extract features and aggregate them with the graph network. Secondly, the self-attention mechanism is utilized to enhance the structure information in the point cloud and the cross-attention mechanism is designed to enhance the corresponding information between the two input point clouds. Based on which, the virtual corresponding points can be generated by a soft pointer based method, and finally, the point cloud registration problem can be solved by implementing the SVD method. Comparison results in ModelNet40 dataset validate that the proposed approach reaches the state-of-the-art in point cloud registration tasks and experiment resutls in KITTI dataset validate the effectiveness of the proposed approach in real applications.Our source code is available at \url{https://github.com/qiaozhijian/VCR-Net.git}
