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Received：16 April 2021，
Revised：2021-6-29，
Accepted：06 July 2021，
Published：16 February 2022
移动端阅览
点云是一种3维表示方式，在广泛应用的同时产生了对点云处理的诸多挑战。其中，点云配准是一项非常值得研究的工作。点云配准旨在将多个点云正确配准到同一个坐标系下，形成更完整的点云。点云配准要应对点云非结构化、不均匀和噪声等干扰，要以更短的时间消耗达到更高的精度，时间消耗和精度往往是矛盾的，但在一定程度上优化是有可能的。点云配准广泛应用于3维重建、参数评估、定位和姿态估计等领域，在自动驾驶、机器人和增强现实等新兴应用上也有点云配准技术的参与。为此，研究者开发了多样巧妙的点云配准方法。本文梳理了一些比较有代表性的点云配准方法并进行分类总结，对比相关工作，尽量覆盖点云配准的各种形式，并对一些方法的细节加以分析介绍。将现有方法归纳为非学习方法和基于学习的方法进行分析。非学习方法分为经典方法和基于特征的方法；基于学习的方法分为结合了非学习方法的部分学习方法和直接的端到端学习方法。本文分别介绍了各类方法的典型算法，并对比总结算法特性，展望了点云配准技术的未来研究方向。
As a 3D representation
point cloud is widely used and brings many challenges to point cloud processing. One of the tasks worth studying is point cloud registration that aims to register multiple point clouds correctly to the same coordinate and form a more complete point cloud. Point cloud registration should deal with the unstructured
uneven
noise
and other interference of the point cloud. It needs a shorter time consumption and achieves a higher accuracy. However
time consumption and precision are often contradictory
but it is optimized to a certain extent is possible. Point cloud registration is widely used in fields such as 3D reconstruction
parameter evaluation
positioning
and posture estimation. Autonomous driving
robotics
augmented reality
and others applications also involve the cloud registration technology. For this reason
various ingenious point cloud registration methods have been developed by researchers. In this paper
several representative point cloud registration methods are sorted out and summarized as a review. Compared with related work
this paper tries to cover various forms of point cloud registration and analyzes the details of several methods. It summarizes the existing methods into nonlearning methods and learning-based methods. Nonlearning methods are divided into classical methods and feature-based methods. Among them
the classic methods include iterative closest point and its variants
normal distributions transform and its variants
and 4-points congruent sets and its variants. Iterative closest point and normal distributions transform and their variants are classical fine registration methods and can achieve a high accuracy
but need a good initial pose. The 4-points congruent sets and its variants are classical coarse registration methods
do not need an initial pose
and can be used as the initial pose for fine registration after this coarse registration. For feature based algorithms
the methods of feature detection
feature description
and feature matching are introduced. They are the main process of a typical point cloud registration method in addition to other steps such as preprocessing of point cloud and calculation and verification of transformation matrix. The features are divided into point-based features
line-based features
surface-based features
and texture-based features. For different features
feature detection
description
and matching are also different
but none of them need an initial position. In addition to registration
these features can also be used for point cloud segmentation
recognition
and other tasks. Similarly
learning-based methods are subdivided into two types: partial learning methods that combine nonlearning components and purely end-to-end learning methods. The partial learning method replaces several components in the nonlearning method with learning-based components and exerts the high speed and high reliability of the learning method
which can bring great improvement to the nonlearning method. This method can also use several learning components designed for other tasks and provide learning components designed for registration tasks for other tasks; thus
it has a high flexibility. Many of these methods are similar to feature-based nonlearning methods and are feature based. However
several methods learn to segment point clouds and then use iterative closest point or normal distributions transform for registration. These partial learning methods have great flexibility
but the data required by partial learning methods are not easy to obtain
and verifying the learning results of partial learning is not easy. The end-to-end learning methods are more convenient to learn
its training data are easier to obtain
and the learning results are easier to verify. The end-to-end method also has a great advantage in speed
which can make full use of the computing power of the graphics processing unit(GPU). Nonlearning methods have lower hardware requirements
are easier to implement
and do not require training. They may not have an advantage in computing speed under the same registration performance
whereas learning-based methods can learn more advanced features in the point cloud
which is very helpful for improving the registration performance but depends on the diversity of the data set and the more advanced deep learning structure. The details of several typical algorithms for each method are introduced
and then the characteristics of these algorithms are compared and summarized. The performance of point cloud registration algorithms is constantly improving
but more point cloud application scenarios also entail higher requirements for point cloud registration
such as the requirements for real-time performance and the effectiveness of noise and lack or repetitive features
and robustness when dealing with unstable point clouds of multiple moving objects. Point cloud registration technology is still a worthy research direction. Point cloud registration technology will inevitably continue to make breakthroughs in speed
precision
and accuracy
and serve more applications.
Aiger D, Mitra N J and Cohen-Or D. 2008. 4-points congruent sets for robust pairwise surface registration. ACM Transactions on Graphics, 27(3): 1-10[DOI:10.1145/1360612.1360684]
Albarelli A, RodolàE, Bergamasco F and Torsello A. 2011. A non-cooperative game for 3D object recognition in cluttered scenes//Proceedings of 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. Hangzhou, China: IEEE: 252-259[ DOI: 10.1109/3DIMPVT.2011.39 http://dx.doi.org/10.1109/3DIMPVT.2011.39 ]
Aoki Y, Goforth H, Srivatsan R A and Lucey S. 2019. PointNetLK: Robust & efficient point cloud registration using PointNet//Proceedings of the 32nd IEEE/CVF Conference on Computer Vision and PatternRecognition. Long Beach, USA: IEEE: 7156-7165[ DOI: 10.1109/CVPR.2019.00733 http://dx.doi.org/10.1109/CVPR.2019.00733 ]
Bello S A, Yu S S, Wang C, Adam J M and Li J. 2020. Review: deep learning on 3D point clouds. Remote Sensing, 12(11): #1729[DOI:10.3390/rs12111729]
Besl P J and Mckay N D. 1992. A method for registration of 3-D shapes. IEEE transactions on pattern analysic and machine intelligence, 14(2): 239-256[DOI:10.1109/34.121791]
Biber P and Strasser W. 2003. The normal distributions transform: a new approach to laser scan matching//Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, USA: IEEE: 2743-2748[ DOI: 10.1109/IROS.2003.1249285 http://dx.doi.org/10.1109/IROS.2003.1249285 ]
Brenner C, Dold C and Ripperda N. 2008. Coarse orientation of terrestrial laser scans in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 63(1): 4-18[DOI:10.1016/j.isprsjprs.2007.05.002]
Bueno M, González-Jorge H, Martínez-Sánchez J and Lorenzo H. 2017. Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes. Automation in Construction, 81: 134-148[DOI:10.1016/j.autcon.2017.06.016]
Charles R Q, Su H, Kaichun M and Guibas L J. 2017. PointNet: deep learning on point sets for 3D classification and segmentation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 77-85[ DOI: 10.1109/CVPR.2017.16 http://dx.doi.org/10.1109/CVPR.2017.16 ]
Chen C S, Hung Y P and Cheng J B. 1999. RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(11): 1229-1234[DOI:10.1109/34.809117]
Chen S L, Nan L L, Xia R B, Zhao J B and Wonka P. 2020. PLADE: a plane-based descriptor for point cloud registration with small overlap. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2530-2540[DOI:10.1109/TGRS.2019.2952086]
Chen Y and Medioni G. 1992. Object modelling by registration of multiple range images. Image and Vision Computing, 10(3): 145-155[DOI:10.1016/0262-8856(92)90066-c]
Cheng L, Chen S, Liu X Q, Xu H, Wu Y, Li M C and Chen Y M. 2018. Registration of laser scanning point clouds: a review. Sensors, 18(5): #1641[DOI:10.3390/s18051641]
Chua C S and Jarvis R. 1997. Point signatures: a new representation for 3D object recognition. International Journal of Computer Vision, 25(1): 63-85[DOI:10.1023/a:1007981719186]
Das A and Waslander S L. 2012. Scan registration with multi-scale k-means normal distributions transform//Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal: IEEE: 2705-2710[ DOI: 10.1109/IROS.2012.6386185 http://dx.doi.org/10.1109/IROS.2012.6386185 ]
Das A and Waslander S L. 2014. Scan registration using segmented region growing NDT. The International Journal of Robotics Research, 33(13): 1645-1663[DOI:10.1177/0278364914539404]
Deng H W, Birdal T and Ilic S. 2018a. PPF-FoldNet: unsupervised learning of rotation invariant 3D local descriptors//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer: 620-638[ DOI: 10.1007/978-3-030-01228-1_37 http://dx.doi.org/10.1007/978-3-030-01228-1_37 ]
Deng H W, Birdal T and Ilic S. 2018b. PPFNet: global context aware local features for robust 3D point matching//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 195-205[ DOI: 10.1109/CVPR.2018.00028 http://dx.doi.org/10.1109/CVPR.2018.00028 ]
Díez Y, Roure F, Lladó X and Salvi J. 2015. A qualitative review on 3D coarse registration methods. ACM Computing Surveys, 47(3): #45[DOI:10.1145/2692160]
Dong Z, Liang F X, Yang B S, Xu Y S, Zang Y F, Li J P, Wang Y, Dai W X, Fan H C, Hyyppä J and Stilla U. 2020. Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 327-342[DOI:10.1016/j.isprsjprs.2020.03.013]
Du A N, Huang X S, Zhang J, Yao L X and Wu Q. 2019. Kpsnet: keypoint detection and feature extraction for point cloud registration//Proceedings of the 26th IEEE International Conference on Image Processing. Taipei, China: IEEE: 2576-2580[ DOI: 10.1109/ICIP.2019.8803365 http://dx.doi.org/10.1109/ICIP.2019.8803365 ]
Duan Y, Yang C C, Chen H, Yan W Z and Li H B. 2021. Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction. Optics Communications, 482: #126567[DOI:10.1016/j.optcom.2020.126567]
Elbaz G, Avraham T and Fischer A. 2017. 3D point cloud registration for localization using a deep neural network auto-encoder//Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 2472-2481[ DOI: 10.1109/CVPR.2017.265 http://dx.doi.org/10.1109/CVPR.2017.265 ]
Feldmar J and Ayache N. 1996. Rigid, affine and locally affine registration of free-form surfaces. International Journal of Computer Vision, 18(2): 99-119[DOI:10.1007/BF00054998]
Fotsing C, Nziengam N and Bobda C. 2020. Large common plansets-4-points congruent sets for point cloud registration. ISPRS International Journal of Geo-Information, 9(11): #647[DOI:10.3390/ijgi9110647]
Frome A, Huber D, Kolluri R, Bülow T and Malik J. 2004. Recognizing objects in range data using regional point descriptors//Proceedings of the 8th European Conference on Computer Vision. Prague, Czech: Springer: 224-237[ DOI: 10.1007/978-3-540-24672-5_18 http://dx.doi.org/10.1007/978-3-540-24672-5_18 ]
Gelfand N, Mitra N J, Guibas L J and Pottmann H. 2005. Robust global registration//Proceedings of the 3rd Eurographics Symposium on Geometry Processing. Vienna, Austria: Eurographics Association: 197-206[ DOI: 10.2312/SGP/SGP05/197-206 http://dx.doi.org/10.2312/SGP/SGP05/197-206 ]
Gold S, Rangarajan A, Lu C P, Pappu S and Mjolsness E. 1998. New algorithms for 2D and 3D point matching: pose estimation and correspondence. Pattern Recognition, 31(8): 1019-1031[DOI:10.1016/S0031-3203(98)80010-1]
Guo Y L, Bennamoun M, Sohel F, Lu M and Wan J W. 2014. 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11): 2270-2287[DOI:10.1109/TPAMI.2014.2316828]
Huang J D, Kwok T H and Zhou C. 2017. V4PCS: volumetric 4PCS algorithm for global registration. Journal of Mechanical Design, 139(11): #111403[DOI:10.1115/1.4037477]
Huang X S, Mei G F and Zhang J. 2020. Feature-metric registration: a fast semi-supervised approach for robust point cloud registration without correspondences//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 11363-11371[ DOI: 10.1109/CVPR42600.2020.01138 http://dx.doi.org/10.1109/CVPR42600.2020.01138 ]
Ilci V and Toth C. 2020. High definition 3D map creation using GNSS/IMU/LiDAR sensor integration to support autonomous vehicle navigation. Sensors, 20(3): #899[DOI:10.3390/s20030899]
Ji S J, Ren Y C, Zhao J, Liu X L and Gao H. 2017. An improved method for registration of point cloud. Optik, 140: 451-458[DOI:10.1016/j.ijleo.2017.01.041]
Ji Z, Zhou F, Tian X, Jiang R X and Chen Y W. 2013. Probabilistic 3D ICP algorithm based on ORB feature//Proceedings of the 3rd IEEE International Conference on Information Science and Technology. Yangzhou, China: IEEE: 300-304[ DOI: 10.1109/ICIST.2013.6747555 http://dx.doi.org/10.1109/ICIST.2013.6747555 ]
Jung S, Song S, Chang M and Park S. 2018. Range image registration based on 2D synthetic images. Computer-Aided Design, 94: 16-27[DOI:10.1016/j.cad.2017.08.001]
Kamousi P, Lazard S, Maheshwari A and Wuhrer S. 2016. Analysis of farthest point sampling for approximating geodesics in a graph. Computational Geometry, 57: 1-7[DOI:10.1016/j.comgeo.2016.05.005]
Kanezaki A, Matsushita Y and Nishida Y. 2018. RotationNet: joint object categorization and pose estimation using multiviews from unsupervisedviewpoints//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 5010-5019[ DOI: 10.1109/CVPR.2018.00526 http://dx.doi.org/10.1109/CVPR.2018.00526 ]
Khoury M, Zhou Q Y and Koltun V. 2017. Learning compact geometric features//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 153-161[ DOI: 10.1109/ICCV.2017.26 http://dx.doi.org/10.1109/ICCV.2017.26 ]
Kurobe A, Sekikawa Y, Ishikawa K and Saito H. 2020. CorsNet: 3D point cloud registration by deep neural network. IEEE Robotics and Automation Letters, 5(3): 3960-3966[DOI:10.1109/LRA.2020.2970946]
Li J X and Lee G H. 2019. USIP: unsupervised stable interest point detection from 3D point clouds//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South):IEEE: 361-370[ DOI: 10.1109/ICCV.2019.00045 http://dx.doi.org/10.1109/ICCV.2019.00045 ]
Liu H K, Zhang Y, Lei L J, Xie H, Li Y and Sun S L. 2020. Hierarchical optimization of 3D point cloud registration. Sensors, 20(23): #6999[DOI:10.3390/s20236999]
Liu W P, Sun J, Li W Y, Hu T and Wang P. 2019. Deep learning on point clouds and its application: a survey. Sensors, 19(19): #4188[DOI:10.3390/s19194188]
Liu X L, Peng X, Yin Y K, Tian J D, Li A M and Zhao X B. 2007. A coarse registration method of range image based on SIFT//Proceedings Volume 6833, Electronic Imaging and Multimedia Technology V. Beijing, China: SPIE: #68330X[ DOI: 10.1117/12.756264 http://dx.doi.org/10.1117/12.756264 ]
Lu J, Liu W, Dong D L and Shao Q. 2015. Point cloud registration algorithm based on NDT with variable size voxel//Proceedings of the 34th Chinese Control Conference. Hangzhou, China: IEEE: 3707-3712[ DOI: 10.1109/ChiCC.2015.7260213 http://dx.doi.org/10.1109/ChiCC.2015.7260213 ]
Lu W X, Wan G W, Zhou Y, Fu X Y, Yuan P F and Song S Y. 2019. DeepVCP: an end-to-end deep neural network for point cloud registration//Proceedings of the 17th IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 12-21[ DOI: 10.1109/ICCV.2019.00010 http://dx.doi.org/10.1109/ICCV.2019.00010 ]
Magnusson M, Lilienthal A and Duckett T. 2007. Scan registration for autonomous mining vehicles using 3D-NDT. Journal of Field Robotics, 24(10): 803-827[DOI:10.1002/rob.20204]
Masuda T, Sakaue K and Yokoya N. 1996. Registration and integration of multiple range images for 3-D model construction//Proceedings of the 13th International Conference on Pattern Recognition. Vienna, Austria: IEEE: 879-883[ DOI: 10.1109/ICPR.1996.546150 http://dx.doi.org/10.1109/ICPR.1996.546150 ]
Maturana D and Scherer S. 2015. 3D convolutional neural networks for landing zone detection from LiDAR//Proceedings of 2015 IEEE International Conference on Robotics and Automation. Seattle, USA: IEEE: 3471-3478[ DOI: 10.1109/ICRA.2015.7139679 http://dx.doi.org/10.1109/ICRA.2015.7139679 ]
Mellado N, Aiger D and Mitra N J. 2014. Super 4PCS fast global pointcloud registration via smart indexing. Computer Graphics Forum, 33(5): 205-215[DOI:10.1111/cgf.12446]
Meng H Y, Gao L, Lai Y K and Manocha D. 2019. VV-Net: Voxel VAE net with group convolutions for point cloud segmentation//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 8499-8507[ DOI: 10.1109/ICCV.2019.00859 http://dx.doi.org/10.1109/ICCV.2019.00859 ]
Mohamad M, Rappaport D and Greenspan M. 2014. Generalized 4-points congruent sets for 3D registration//Proceedings of the 2nd International Conference on 3D Vision. Tokyo, Japan: IEEE: 83-90[ DOI: 10.1109/3DV.2014.21 http://dx.doi.org/10.1109/3DV.2014.21 ]
Pavlov A L, Ovchinnikov G W, Derbyshev D Y, Tsetserukou D and Oseledets I V. 2018. AA-ICP: iterative closest point with anderson acceleration//Proceedings of 2018 IEEE International Conference on Robotics and Automation. Brisbane, Australia: IEEE: 3407-3412[ DOI: 10.1109/ICRA.2018.8461063 http://dx.doi.org/10.1109/ICRA.2018.8461063 ]
Pomerleau F, Colas F and Siegwart R. 2015. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics, 4(1): 1-104[DOI:10.1561/2300000035]
Prakhya S M, Liu B B and Lin W S. 2016. Detecting keypoint sets on 3D point clouds via histogram of normal orientations. Pattern Recognition Letters, 83: 42-48[DOI:10.1016/j.patrec.2016.06.002]
Prokop M, Shaikh S A and Kim K S. 2020. Low overlapping point cloud registration using line features detection. Remote Sensing, 12(1): #61[DOI:10.3390/RS12010061]
Qi C R, Su H, Nieβner M, Dai A, Yan M Y and Guibas L J. 2016. Volumetric and multi-view CNNs for object classification on 3D data//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 5648-5656[ DOI: 10.1109/CVPR.2016.609 http://dx.doi.org/10.1109/CVPR.2016.609 ]
Qi C R, Yi L, Su H and Guibas L J. 2017. PointNet++: deep hierarchical feature learning on point sets in a metric space//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc. : 5105-5114
Ren Y and Zhou F C. 2015. A 3D point cloud registration algorithm based on feature points//Proceedings of the 1st International Conference on Information Sciences, Machinery, Materials and Energy. Chongqing, China: Atlantis Press: 802-806[ DOI: 10.2991/icismme-15.2015.168 http://dx.doi.org/10.2991/icismme-15.2015.168 ]
Rusinkiewicz S and Levoy M. 2001. Efficient variants of the ICP algorithm//Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. Quebec City, Canada: IEEE: 145-152[ DOI: 10.1109/IM.2001.924423 http://dx.doi.org/10.1109/IM.2001.924423 ]
Rusu R B, Blodow N and Beetz M. 2009. Fast point feature histograms (FPFH) for 3D registration//Proceedings of 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE: 3212-3217[ DOI: 10.1109/ROBOT.2009.5152473 http://dx.doi.org/10.1109/ROBOT.2009.5152473 ]
Rusu R B, Blodow N, Marton Z C and Beetz M. 2008. Aligning point cloud views using persistent feature histograms//Proceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: IEEE: 3384-3391[ DOI: 10.1109/IROS.2008.4650967 http://dx.doi.org/10.1109/IROS.2008.4650967 ]
Segal A, Hähnel D and Thrun S. 2009. Generalized-ICP//Robotics: Science and Systems V. Seattle, USA: University of Washington[ DOI: 10.15607/RSS.2009.V.021 http://dx.doi.org/10.15607/RSS.2009.V.021 ]
Simon D A, Hebert M and Kanade T. 1995. Techniques for fast and accurate intrasurgical registration. Journal of Image Guided Surgery, 1(1): 17-29[DOI:10.3109/10929089509106822]
Sipiran I and Bustos B. 2011. Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. The Visual Computer, 27(11): 963-976[DOI:10.1007/s00371-011-0610-y]
Stamos I and Allen P K. 2002. Geometry and texture recovery of scenes of large scale. Computer Vision and Image Understanding, 88(2): 94-118[DOI:10.1006/cviu.2002.0963]
Su B Y, Chu X Y and Sheng M. 2019. RGB-D point cloud registration method via homomorphic filtering. Journal of Image and Graphics, 24(11): 1985-1997
苏本跃, 储小玉, 盛敏. 2019. 同态滤波下RGB-D点云配准方法. 中国图象图形学报, 24(11): 1985-1997[DOI:10.11834/jig.190011]
Su H, Maji S, Kalogerakis E and Learned-Miller E. 2015. Multi-view convolutional neural networks for 3D shape recognition//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 945-953[ DOI: 10.1109/ICCV.2015.114 http://dx.doi.org/10.1109/ICCV.2015.114 ]
Sultani Z N and Ghani R F. 2015. Kinect 3D point cloud live video streaming. Procedia Computer Science, 65: 125-132[DOI:10.1016/j.procs.2015.09.090]
Tao W Y, Hua X H, Chen Z P and Tian P J. 2020. Fast and automatic registration of terrestrial point clouds using 2D line features. Remote Sensing, 12(8): #1283[DOI:10.3390/RS12081283]
Theiler P W, Wegner J D and Schindler K. 2014. Keypoint-based 4-points congruent sets——automated marker-less registration of laser scans. ISPRS Journal of Photogrammetry and Remote Sensing, 96: 149-163[DOI:10.1016/j.isprsjprs.2014.06.015]
Tian B, Jiang P L, Zhang X T, Zhang Y L and Wang F. 2016. A novelfeature point detection algorithm of unstructured 3D point cloud//Proceedings of the 12th International Conference on Intelligent Computing. Lanzhou, China: Springer: 736-744[ DOI: 10.1007/978-3-319-42297-8_68 http://dx.doi.org/10.1007/978-3-319-42297-8_68 ]
Truong G, Gilani S Z, Islam S M S and Suter D. 2019. Fast point cloud registration using semantic segmentation//Proceedings of 2019 Digital Image Computing: Techniques and Applications. Perth, WA, Australia: IEEE: 1-8[ DOI: 10.1109/DICTA47822.2019.8945870 http://dx.doi.org/10.1109/DICTA47822.2019.8945870 ]
Wang F, Ye Y X, Hu X Y and Shan J. 2016. Point cloud registration by combining shape and intensity contexts//Proceedings of the 9th IAPR Workshop on Pattern Recognition in Remote Sensing. Cancun, Mexico: IEEE: 1-6[ DOI: 10.1109/PRRS.2016.7867025 http://dx.doi.org/10.1109/PRRS.2016.7867025 ]
Wang Y and Solomon J. 2019. Deep closest point: learning representations for point cloud registration//Proceedings of the 17th IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 3522-3531[ DOI: 10.1109/ICCV.2019.00362 http://dx.doi.org/10.1109/ICCV.2019.00362 ]
Wang Y, Sun Y B, Liu Z W, Sarma S E, Bronstein M M and Solomon J M. 2019. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38(5): #146[DOI:10.1145/3326362]
Xu Y S, Boerner R, Yao W, Hoegner L and Stilla U. 2019. Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 106-123[DOI:10.1016/j.isprsjprs.2019.02.015]
Yang B S, Dong Z, Liang F X and Liu Y. 2016. Automatic registration of large-scale urban scene point clouds based on semantic feature points. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 43-58[DOI:10.1016/j.isprsjprs.2015.12.005]
Yang B S and Zang Y F. 2014. Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 109-121[DOI:10.1016/j.isprsjprs.2014.05.012]
Yang J L, Li H D and Jia Y D. 2013. Go-ICP: solving 3D registration efficiently and globally optimally//Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE: 1457-1464[ DOI: 10.1109/ICCV.2013.184 http://dx.doi.org/10.1109/ICCV.2013.184 ]
Yang Y, Chen W L, Wang M Y, Zhong D X and Du S Y. 2020. Color point cloud registration based on supervoxel correspondence. IEEE Access, 8: 7362-7372[DOI:10.1109/ACCESS.2020.2963987]
Yew Z J and Lee G H. 2018. 3DFeat-Net: weakly supervised local3D features for point cloud registration//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer: 630-646[ DOI: 10.1007/978-3-030-01267-0_37 http://dx.doi.org/10.1007/978-3-030-01267-0_37 ]
Yew Z J and Lee G H. 2020. RPM-Net: robust point matching using learned features//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 11821-11830[ DOI: 10.1109/CVPR42600.2020.01184 http://dx.doi.org/10.1109/CVPR42600.2020.01184 ]
Yuan Y J, Borrmann D, Hou J W, Ma Y X, Nüchter A and Schwertfeger S. 2021. Self-supervised point set local descriptors for point cloud registration. Sensors, 21(2): #486[DOI:10.3390/s21020486]
ZaganidisA, Magnusson M, Duckett T and Cielniak G. 2017. Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE: 4064-4069[ DOI: 10.1109/IROS.2017.8206262 http://dx.doi.org/10.1109/IROS.2017.8206262 ]
Zaganidis A, Sun L, Duckett T and Cielniak G. 2018. Integrating deep semantic segmentation into 3-D point cloud registration. IEEE Robotics and Automation Letters, 3(4): 2942-2949[DOI:10.1109/LRA.2018.2848308]
Zeng A, Song S R, Nieβner M, Fisher M, Xiao J X and Funkhouser T. 2017. 3DMatch: learning local geometric descriptors from RGB-D reconstructions//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 199-208[ DOI: 10.1109/CVPR.2017.29 http://dx.doi.org/10.1109/CVPR.2017.29 ]
Zhang X F, Gao R Q, Sun Q and Cheng J Y. 2019. An automated rectification method for unmanned aerial vehicle LiDAR point cloud data based on laser intensity. Remote Sensing, 11(7): #811[DOI:10.3390/rs11070811]
Zhong Y. 2009. Intrinsic shape signatures: a shape descriptor for 3D object recognition//Proceedings of the 12th IEEE International Conference on Computer Vision Workshops. Kyoto, Japan: IEEE: 689-696[ DOI: 10.1109/ICCVW.2009.5457637 http://dx.doi.org/10.1109/ICCVW.2009.5457637 ]
Zhou Y and Tuzel O. 2018. VoxelNet: end-to-end learning for point cloud based 3D object detection//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE: Salt Lake City, USA: 4490-4499[ DOI: 10.1109/CVPR.2018.00472 http://dx.doi.org/10.1109/CVPR.2018.00472 ]
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