点云配准论文

点云(刚性)配准，求解两个(具有overlap的)点云P, Q之间的变换(旋转矩阵和平移向量)，使得点云P, Q对齐(可以通俗的理解为使P, Q的坐标处于同一坐标系下)。点云配准在无人驾驶、三维重建等领域具有广泛的应用。

本文整理了点云配准相关的论文，既包括基于深度学习的点云配准算法，也包括部分传统配准算法(ICP, GoICP, FGR等)。先上一张点云配准的发展脉络图Figure 1。但是需要注意两点: (1) Figure 1中列出了方法并不全面，仅选取了部分代表性的点云配准方法, 更多论文请参考下面的列表; (2) Figure 1中记录的同一年内的配准方法的提出顺序可能有错误。

• Point Cloud Registration using Representative Overlapping Points [arXiv 2021; PyTorch]
• Shape registration in the time of transformers [arXiv 2021]
• 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching [arXiv 2021]
• FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration [arXiv 2021]
• Generalisable and distinctive 3D local
  deep descriptors for point cloud registration [arXiv 2021]
• Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration [arXiv 2021]
• 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning [arXiv 2021; Github]
• R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method [arXiv 2021; Github]
• OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration [arXiv 2021]
• UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering [arXiv 2021; PyTorch]
• PREDATOR: Registration of 3D Point Clouds with Low Overlap [CVPR 2021; PyTorch]
• SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration [CVPR 2021; Github]
• PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency [CVPR 2021; PyTorch]
• Robust Point Cloud Registration Framework Based on Deep Graph Matching [CVPR 2021; Github]
• Fast and Robust Iterative Closest Point [TPAMI 2021; Github]
• RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation [CVPR 2021]
• ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning [CVPR 2021; PyTorch]
• Deep Global Registration [CVPR 2020; PyTorch]
• 3DRegNet: A Deep Neural Network for 3D Point Registration [CVPR 2020; Tensorflow]
• D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [CVPR 2020; Tensorflow, PyTorch]
• Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences [CVPR 2020; PyTorch]
• RPM-Net: Robust Point Matching using Learned Features [CVPR 2020; PyTorch]
• Learning multiview 3D point cloud registration [CVPR 2020; PyTorch]
• DeepGMR: Learning Latent Gaussian Mixture Models for Registration [ECCV 2020; PyTorch]
• Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration [ECCV 2020; PyTorch]
• Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features [Remote Sensing 2020]
• PCRNet: Point Cloud Registration Network using PointNet Encoding [arXiv 2020; PyTorch, Tensorflow]]
• TEASER: Fast and Certifiable Point Cloud Registration [arXiv 2020; Github]
• PRNet: Self-Supervised Learning for Partial-to-Partial Registration [NeurIPS 2019; PyTorch]
• Deep Closest Point: Learning Representations for Point Cloud Registration [ICCV 2019; PyTorch]
• DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration [ICCV 2019]
• USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds [ICCV 2019; PyTorch]
• 3D Local Features for Direct Pairwise Registration [CVPR 2019]
• DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds [CVPR 2019; Github]
• PointNetLK: Point Cloud Registration using PointNet [CVPR 2019; PyTorch]
• PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [CVPR 2018]
• 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [ECCV 2018; Tensorflow]
• PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [ECCV 2018]
• Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration [3DV 2018]
• Guaranteed Outlier Removal for Point Cloud Registration with Correspondences [TPAMI 2018]
• 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions [CVPR 2017; project]
• 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder [CVPR 2017; github]
• Fast Global Registration [ECCV 2016; Github]
• Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration [TPAMI 2015; Github]
• Fast point feature histograms (FPFH) for 3D registration [ICRA 2009]
• A method for registration of 3-D shapes [TPAMI 1992]
• Least-squares fitting of two 3-D point sets [TPAMI 1987]

更多点云相关(分类、分割、检测等)的文章列表详见https://github.com/zhulf0804/3D-PointCloud。

最后，安利一下自己的基于深度学习的点云配准工作Point Cloud Registration using Representative Overlapping Points, 代码也已经开源, https://github.com/zhulf0804/ROPNet。欢迎大家一块交流学习~