12.3.4.1.2 ICP, Iterative Closest Point Registeration for Point Clouds

Chapter Contents (Back)
Registration, 3-D. Surface Matching. Matching, Surfaces. Registration. Lidar Registration. Point Clouds. Point Cloud Registration. 3-D.
See also Registration or Multiple Range Images, Range Image Registration.
See also Register Terrestrial Laser Scanner Point Cloud Data, TLS.

Liu, Y.H.[Yong-Huai],
Automatic registration of overlapping 3D point clouds using closest points,
IVC(24), No. 7, July 2006, pp. 762-781.
Elsevier DOI 0608
3D point clouds; SoftAssign; EMICP; Combinatorial optimization; Entropy maximization; Deterministic annealing; Optimised k-D tree BibRef

Liu, Y.H.[Yong-Huai],
Constraints for closest point finding,
PRL(29), No. 7, 1 May 2008, pp. 841-851.
Elsevier DOI 0804
Closest point criterion; Free form shape matching; Orientation constraint; Rigidity constraint; Matching error constraint; Possible point match evaluation BibRef

Xie, Z.X.[Ze-Xiao], Xu, S.[Shang], Li, X.Y.[Xu-Yong],
A high-accuracy method for fine registration of overlapping point clouds,
IVC(28), No. 4, April 2010, pp. 563-570.
Elsevier DOI 1002
Fine registration; ICP algorithm; Dual interpolating point-to-surface; Surface fitting; Auxiliary pair BibRef

Lin, B.W.[Bao-Wei], Tamaki, T.[Toru], Zhao, F.[Fangda], Raytchev, B.[Bisser], Kaneda, K.[Kazufumi], Ichii, K.[Koji],
Scale alignment of 3D point clouds with different scales,
MVA(25), No. 8, November 2014, pp. 1989-2002.
Springer DOI 1411
BibRef
Earlier: A1, A2, A4, A5, A6, Only:
Scale ratio ICP for 3D point clouds with different scales,
ICIP13(2217-2221)
IEEE DOI 1402
3D point cloud;ICP;registration;scale ratio;spin images BibRef

Tamaki, T.[Toru], Tanigawa, S.[Shunsuke], Ueno, Y.J.[Yu-Ji], Raytchev, B.[Bisser], Kaneda, K.[Kazufumi],
Scale Matching of 3D Point Clouds by Finding Keyscales with Spin Images,
ICPR10(3480-3483).
IEEE DOI 1008
BibRef

Xie, J.[Jun], Hsu, Y.F.[Yu-Feng], Feris, R.S.[Rogerio Schmidt], Sun, M.T.[Ming-Ting],
Fine registration of 3D point clouds fusing structural and photometric information using an RGB-D camera,
JVCIR(32), No. 1, 2015, pp. 194-204.
Elsevier DOI 1511
ICP BibRef

Li, W.M.[Wei-Min], Song, P.F.[Peng-Fei],
A modified ICP algorithm based on dynamic adjustment factor for registration of point cloud and CAD model,
PRL(65), No. 1, 2015, pp. 88-94.
Elsevier DOI 1511
Point cloud BibRef

Huang, X.S.[Xiao-Shui], Zhang, J.[Jian], Fan, L.X.[Li-Xin], Wu, Q.[Qiang], Yuan, C.[Chun],
A Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structures,
IP(26), No. 7, July 2017, pp. 3261-3276.
IEEE DOI 1706
BibRef
Earlier: A1, A2, A4, A3, A5:
A Coarse-to-Fine Algorithm for Registration in 3D Street-View Cross-Source Point Clouds,
DICTA16(1-6)
IEEE DOI 1701
Feature extraction, Image sensors, Iterative closest point algorithm, Robustness, Sensors, Solid modeling, Cross-source, graph matching, macro/micro, point cloud registration. Complexity theory BibRef

Huang, X.S.[Xiao-Shui], Zhang, J.[Jian], Wu, Q.[Qiang], Fan, L.X.[Li-Xin], Yuan, C.[Chun],
A Coarse-to-Fine Algorithm for Matching and Registration in 3D Cross-Source Point Clouds,
CirSysVideo(28), No. 10, October 2018, pp. 2965-2977.
IEEE DOI 1811
Sensors, Feature extraction, Iterative closest point algorithm, Transforms, smart city BibRef

Peng, F.R.[Fu-Rong], Wu, Q.[Qiang], Fan, L.X.[Li-Xin], Zhang, J.[Jian], You, Y.[Yu], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu],
Street view cross-sourced point cloud matching and registration,
ICIP14(2026-2030)
IEEE DOI 1502
Accuracy BibRef

Lei, H., Jiang, G., Quan, L.,
Fast Descriptors and Correspondence Propagation for Robust Global Point Cloud Registration,
IP(26), No. 8, August 2017, pp. 3614-3623.
IEEE DOI 1707
eigenvalues and eigenfunctions, image registration, iterative methods, Kinect, correspondence propagation, descriptor-based methods, distance errors, eigenvalue-based descriptor, fast descriptors, global algorithms, iterative closest point algorithm, low-resolution point clouds, BibRef

Vongkulbhisal, J.[Jayakorn], de la Torre, F.[Fernando], Costeira, J.P.[Joo Paulo],
Discriminative Optimization: Theory and Applications to Computer Vision,
PAMI(41), No. 4, April 2019, pp. 829-843.
IEEE DOI 1903
BibRef
Earlier:
Discriminative Optimization: Theory and Applications to Point Cloud Registration,
CVPR17(3975-3983)
IEEE DOI 1711
Cost function, Iterative closest point algorithm, Cameras, Pose estimation, Training data, machine learning. Convergence, Iterative closest point algorithm, Robustness, BibRef

Vongkulbhisal, J.[Jayakorn], Ugalde, B.I., de la Torre, F.[Fernando], Costeira, J.P.[Joo Paulo],
Inverse Composition Discriminative Optimization for Point Cloud Registration,
CVPR18(2993-3001)
IEEE DOI 1812
Shape, Optimization, Integrated circuits, Training data, Annealing, Iterative closest point algorithm BibRef

Jauer, P.[Philipp], Kuhlemann, I.[Ivo], Bruder, R.[Ralf], Schweikard, A.[Achim], Ernst, F.[Floris],
Efficient Registration of High-Resolution Feature Enhanced Point Clouds,
PAMI(41), No. 5, May 2019, pp. 1102-1115.
IEEE DOI 1904
Iterative closest point algorithm, Color, Robustness, Sensors, Cameras, Cloud computing, Point cloud, Coulomb's law BibRef

Li, J.Y.[Jia-Yuan], Zhao, P.C.[Peng-Cheng], Hu, Q.W.[Qing-Wu], b Ai, M.Y.[Ming-Yao],
Robust point cloud registration based on topological graph and Cauchy weighted lq-norm,
PandRS(160), 2020, pp. 244-259.
Elsevier DOI 2001
Point Cloud Registration (PCR), Coarse-to-fine registration, Feature correspondence, Iterative Closest Point (ICP), Robust estimation BibRef

Combs, B.[Benoit], Prima, S.[Sylvain],
An efficient EM-ICP algorithm for non-linear registration of large 3D point sets,
CVIU(191), 2020, pp. 102854.
Elsevier DOI 2002
Point sets, Surface, Non-linear registration, Alignment, ICP, EM, EM-ICP BibRef

Liao, Q.F.[Qian-Fang], Sun, D.[Da], Andreasson, H.[Henrik],
Point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization,
PAMI(43), No. 9, September 2021, pp. 3229-3246.
IEEE DOI 2108
Measurement, Optimization, Iterative closest point algorithm, Quality assessment, branch-and-bound BibRef


Gu, X.D.[Xiao-Dong], Tang, C.Z.[Cheng-Zhou], Yuan, W.H.[Wei-Hao], Dai, Z.Z.[Zuo-Zhuo], Zhu, S.[Siyu], Tan, P.[Ping],
RCP: Recurrent Closest Point for Point Cloud,
CVPR22(8206-8216)
IEEE DOI 2210
Point cloud compression, Measurement, Costs, Motion estimation, Neural networks, Estimation, RGBD sensors and analytics BibRef

Jiang, H.[Haobo], Shen, Y.[Yaqi], Xie, J.[Jin], Li, J.[Jun], Qian, J.J.[Jian-Jun], Yang, J.[Jian],
Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration,
ICCV21(6108-6117)
IEEE DOI 2203
Point cloud compression, Training, Measurement, Solid modeling, Iterative closest point algorithm, Transforms, Stereo, BibRef

Jubran, I.[Ibrahim], Maalouf, A.[Alaa], Kimmel, R.[Ron], Feldman, D.[Dan],
Provably Approximated Point Cloud Registration,
ICCV21(13249-13258)
IEEE DOI 2203
Point cloud compression, Iterative closest point algorithm, Economic indicators, Approximation algorithms, Cost function, Vision for robotics and autonomous vehicles BibRef

Favre, K.[Ketty], Pressigout, M.[Muriel], Marchand, E.[Eric], Morin, L.[Luce],
A Plane-based Approach for Indoor Point Clouds Registration,
ICPR21(7072-7079)
IEEE DOI 2105
Measurement, Laser radar, Iterative closest point algorithm, Autonomous systems, Registers BibRef

Franzini, M., Manzino, A.M., Casella, V.,
Weighted ICP Point Clouds Registration By Segmentation Based On Eigenfeatures Clustering,
ISPRS20(B2:217-226).
DOI Link 2012
BibRef

Lu, W., Wan, G., Zhou, Y., Fu, X., Yuan, P., Song, S.,
DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration,
ICCV19(12-21)
IEEE DOI 2004
feature extraction, geometry, image matching, image registration, learning (artificial intelligence), neural nets, DeepVCP, Iterative closest point algorithm BibRef

Wang, Y., Solomon, J.,
Deep Closest Point: Learning Representations for Point Cloud Registration,
ICCV19(3522-3531)
IEEE DOI 2004
computational geometry, feature extraction, graph theory, image registration, iterative methods, Task analysis BibRef

Vlaminck, M., Luong, H., Philips, W.,
Surface-Based GICP,
CRV18(262-268)
IEEE DOI 1812
Surface reconstruction, Iterative closest point algorithm, Cost function, Sensors, surface reconstruction BibRef

Liu, J.[Jian], Shang, X.Q.[Xiu-Qin], Yang, S.Z.[Shu-Zhan], Shen, Z.[Zhen], Liu, X.[Xiwei], Xiong, G.[Gang], Nyberg, T.R.[Timo R.],
Research on Optimization of Point Cloud Registration ICP Algorithm,
PSIVTWS17(81-90).
Springer DOI 1806
BibRef

Vlaminck, M., Luong, H., Philips, W.,
Multi-resolution ICP for the efficient registration of point clouds based on octrees,
MVA17(334-337)
DOI Link 1708
Iterative closest point algorithm, Measurement, Multiresolution analysis, Octrees, Vegetation BibRef

Guan, S., Li, G., Xie, X., Wang, Z.,
Bi-direction ICP: Fast registration method of point clouds,
MVA17(129-132)
DOI Link 1708
Bidirectional control, Iterative closest point algorithm, Organizations, Solid modeling, Spinning, Two, dimensional, displays BibRef

Zhang, A.L., Zhang, B.X., Song, C.J., Shen, D.P., Zhu, E.G., Dong, F.S.,
Viewpoint calibration method based on point features for point cloud fusion,
ICIP17(2224-2228)
IEEE DOI 1803
Calibration, Feature extraction, Iterative closest point algorithm, Robots, Robustness, viewpoint BibRef

Agarwal, S., Bhowmick, B.,
3D point cloud registration with shape constraint,
ICIP17(2199-2203)
IEEE DOI 1803
Force, Genetic algorithms, Iterative closest point algorithm, Shape, gravitational approach BibRef

Malleus, L., Fisichella, T., Lingrand, D., Precioso, F., Gros, N., Noutary, Y., Robert, L., Samoun, L.,
KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans,
Multiview17(2495-2502)
IEEE DOI 1802
Clustering algorithms, Detectors, Feature extraction, Iterative closest point algorithm, BibRef

Straub, J., Campbell, T., How, J.P., Fisher, J.W.,
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures,
CVPR17(2403-2412)
IEEE DOI 1711
Gaussian distribution, Iterative closest point algorithm, Mixture models, Optimization, Robustness, Surface treatment BibRef

Elbaz, G., Avraham, T., Fischer, A.,
3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder,
CVPR17(2472-2481)
IEEE DOI 1711
Algorithm design and analysis, Iterative closest point algorithm, Neural networks, Robustness BibRef

Glai, B., Nagy, B., Benedek, C.,
Crossmodal point cloud registration in the Hough space for mobile laser scanning data,
ICPR16(3374-3379)
IEEE DOI 1705
Iterative closest point algorithm, Laser radar, Mobile communication, Sensor phenomena and characterization, Urban, areas BibRef

Zhu, J., Wang, D., Bai, X., Lu, H., Jin, C., Li, Z.,
Registration of Point Clouds Based on the Ratio of Bidirectional Distances,
3DV16(102-107)
IEEE DOI 1701
Iterative closest point algorithm BibRef

Eggert, D., Dalyot, S.,
Octree-Based SIMD Strategy for ICP Registration and Alignment of 3D Point Clouds,
AnnalsPRS(I-3), No. 2012, pp. 105-110.
HTML Version. 1209
BibRef

Toldo, R.[Roberto], Beinat, A.[Alberto], Crosilla, F.[Fabio],
Global registration of multiple point clouds embedding the Generalized Procrustes Analysis into an ICP framework,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef

Beinat, A.[Alberto], Crosilla, F.[Fabio], Sepic, F.,
Automatic morphological pre-alignment and global hybrid registration of close range images,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Register Terrestrial Laser Scanner Point Cloud Data, TLS .


Last update:Mar 21, 2023 at 18:34:39