QGORE: Quadratic-Time Guaranteed Outlier Removal for Point Cloud Registration
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[1] Zhongzhen Luo,et al. SKP: Semantic 3D Keypoint Detection for Category-Level Robotic Manipulation , 2022, IEEE Robotics and Automation Letters.
[2] Qingwu Hu,et al. Robust symmetric iterative closest point , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.
[3] Zhen Dong,et al. You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors , 2021, ACM Multimedia.
[4] Jiri Matas,et al. Marginalizing Sample Consensus , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Guang Chen,et al. HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] Jiri Matas,et al. VSAC: Efficient and Accurate Estimator for H and F , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[7] Jiri Matas,et al. USACv20: robust essential, fundamental and homography matrix estimation , 2021, ArXiv.
[8] Chiew-Lan Tai,et al. PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Xiaoshui Huang,et al. A comprehensive survey on point cloud registration , 2021, ArXiv.
[10] Yue Pan,et al. MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[11] Qingwu Hu,et al. Point Cloud Registration Based on 1-point RANSAC and Scale-annealing Biweight Estimation , 2020 .
[12] Bo Yang,et al. SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Yinlong Liu,et al. RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor , 2020, NeurIPS.
[14] Qingwu Hu,et al. GESAC: Robust graph enhanced sample consensus for point cloud registration , 2020 .
[15] Jan Kautz,et al. DeepGMR: Learning Latent Gaussian Mixture Models for Registration , 2020, ECCV.
[16] Qingwu Hu,et al. Robust Geometric Model Estimation Based on Scaled Welsch q-Norm , 2020, IEEE Transactions on Geoscience and Remote Sensing.
[17] Torsten Sattler,et al. Making Affine Correspondences Work in Camera Geometry Computation , 2020, ECCV.
[18] Bailin Deng,et al. Fast and Robust Iterative Closest Point , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Vladlen Koltun,et al. Deep Global Registration , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Long Quan,et al. D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Osamu Hirose,et al. A Bayesian Formulation of Coherent Point Drift , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Heng Yang,et al. TEASER: Fast and Certifiable Point Cloud Registration , 2020, IEEE Transactions on Robotics.
[23] F. Fraundorfer,et al. Minimal Solutions for Relative Pose With a Single Affine Correspondence , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Qingwu Hu,et al. RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform , 2019, IEEE Transactions on Image Processing.
[25] Jiri Matas,et al. MAGSAC++, a Fast, Reliable and Accurate Robust Estimator , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Cyrill Stachniss,et al. SuMa++: Efficient LiDAR-based Semantic SLAM , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[27] Vladlen Koltun,et al. Fully Convolutional Geometric Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[28] L. Carlone,et al. Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection , 2019, IEEE Robotics and Automation Letters.
[29] Szymon Rusinkiewicz,et al. A symmetric objective function for ICP , 2019, ACM Trans. Graph..
[30] Yanning Zhang,et al. A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Jiri Matas,et al. Progressive NAPSAC: sampling from gradually growing neighborhoods , 2019, ArXiv.
[32] Shiyu Song,et al. DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[33] Xin Li,et al. LO-Net: Deep Real-Time Lidar Odometry , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Slobodan Ilic,et al. 3D Local Features for Direct Pairwise Registration , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Rama Chellappa,et al. 3DRegNet: A Deep Neural Network for 3D Point Registration , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Jiaxin Li,et al. USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[37] Yasuhiro Aoki,et al. PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Tat-Jun Chin,et al. Practical optimal registration of terrestrial LiDAR scan pairs , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[39] Andreas Wieser,et al. The Perfect Match: 3D Point Cloud Matching With Smoothed Densities , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Dietrich Paulus,et al. MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation , 2018, GCPR.
[41] Yinlong Liu,et al. Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search , 2018, ECCV.
[42] Jonathan Tompson,et al. Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning , 2018, NeurIPS.
[43] Levente Hajder,et al. Efficient Recovery of Essential Matrix From Two Affine Correspondences , 2018, IEEE Transactions on Image Processing.
[44] Jiri Matas,et al. MAGSAC: Marginalizing Sample Consensus , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Jean-Emmanuel Deschaud,et al. IMLS-SLAM: Scan-to-Model Matching Based on 3D Data , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[46] Jacob T. Browne,et al. ConsensUs , 2018, Definitions.
[47] Tat-Jun Chin,et al. Guaranteed Outlier Removal for Point Cloud Registration with Correspondences , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Dzmitry Tsetserukou,et al. AA-ICP: Iterative Closest Point with Anderson Acceleration , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[49] Jiri Matas,et al. Graph-Cut RANSAC , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[50] Jonathan T. Barron,et al. A General and Adaptive Robust Loss Function , 2017, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Danni Ai,et al. Convex hull indexed Gaussian mixture model (CH-GMM) for 3D point set registration , 2016, Pattern Recognit..
[52] Vladlen Koltun,et al. Fast Global Registration , 2016, ECCV.
[53] Carolina Raposo,et al. Theory and Practice of Structure-From-Motion Using Affine Correspondences , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Jiaolong Yang,et al. Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Matthias Nießner,et al. 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Ji Zhang,et al. LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.
[57] Anders P. Eriksson,et al. Fast Rotation Search with Stereographic Projections for 3D Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[58] Jan-Michael Frahm,et al. USAC: A Universal Framework for Random Sample Consensus , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[59] Mohammed Bennamoun,et al. Rotational Projection Statistics for 3D Local Surface Description and Object Recognition , 2013, International Journal of Computer Vision.
[60] Lena Maier-Hein,et al. Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Lionel Moisan,et al. Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers , 2012, Image Process. Line.
[62] B. Vemuri,et al. Robust Point Set Registration Using Gaussian Mixture Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[63] Radu Bogdan Rusu,et al. 3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.
[64] Yu Zhong,et al. Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[65] Andriy Myronenko,et al. Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[66] Nico Blodow,et al. Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.
[67] Joachim Hertzberg,et al. Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT , 2009, 2009 IEEE International Conference on Robotics and Automation.
[68] Jiri Matas,et al. Optimal Randomized RANSAC , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[69] Reinhard Klein,et al. Eurographics Symposium on Point-based Graphics (2006) Octree-based Point-cloud Compression , 2022 .
[70] Jiri Matas,et al. Randomized RANSAC with sequential probability ratio test , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[71] Jiri Matas,et al. Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[72] Pavel Krsek,et al. Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm , 2005, Image Vis. Comput..
[73] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.
[74] Jiri Matas,et al. Randomized RANSAC with Td, d test , 2004, Image Vis. Comput..
[75] Hui Chen,et al. 3D free-form object recognition in range images using local surface patches , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[76] Peter Biber,et al. The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).
[77] Jiri Matas,et al. Locally Optimized RANSAC , 2003, DAGM-Symposium.
[78] Marc Levoy,et al. Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.
[79] Michael J. Black,et al. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision , 1996, International Journal of Computer Vision.
[80] R. Horst,et al. Global Optimization: Deterministic Approaches , 1992 .
[81] Paul J. Besl,et al. A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[82] Gérard G. Medioni,et al. Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.
[83] Gian-Carlo Rota,et al. Introduction to robust and quasi-robust statistical methods: W. J. Rey, Springer, 1983, 237 pp , 1986 .
[84] W. Rey. Introduction to Robust and Quasi-Robust Statistical Methods , 1983 .
[85] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[86] Qingwu Hu,et al. LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching , 2022, IEEE Transactions on Geoscience and Remote Sensing.
[87] R. Chellappa,et al. 3DRegNet: A Deep Neural Network for 3D Point Registration , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[88] Weidong Yang,et al. Ranking 3D feature correspondences via consistency voting , 2019, Pattern Recognit. Lett..
[89] D. Suter,et al. The Maximum Consensus Problem , 2017 .
[90] M. Pauly,et al. Sparse Iterative Closest Point , 2013, Comput. Graph. Forum.
[91] Matthijs C. Dorst. Distinctive Image Features from Scale-Invariant Keypoints , 2011 .
[92] Aleksandr V. Segal,et al. Generalized-ICP , 2009, Robotics: Science and Systems.
[93] Slawomir J. Nasuto,et al. NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag , 2002, BMVC.
[94] Jiri Matas,et al. Randomized RANSAC with T(d, d) test , 2002, BMVC.
[95] P. Holland,et al. Robust regression using iteratively reweighted least-squares , 1977 .