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
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.[João 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.[João 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
Combès, 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
Wang, W.[Wei],
Zhang, Y.[Yi],
Ge, G.[Gengyu],
Yang, H.[Huan],
Wang, Y.[Yue],
A New Approach toward Corner Detection for Use in Point Cloud
Registration,
RS(15), No. 13, 2023, pp. 3375.
DOI Link
2307
BibRef
Wang, H.P.[Hai-Ping],
Liu, Y.[Yuan],
Hu, Q.Y.[Qing-Yong],
Wang, B.[Bing],
Chen, J.G.[Jian-Guo],
Dong, Z.[Zhen],
Guo, Y.L.[Yu-Lan],
Wang, W.P.[Wen-Ping],
Yang, B.[Bisheng],
RoReg: Pairwise Point Cloud Registration With Oriented Descriptors
and Local Rotations,
PAMI(45), No. 8, August 2023, pp. 10376-10393.
IEEE DOI
2307
Feature extraction, Detectors, Point cloud compression, Training,
Estimation, Pipelines, Feature detection, 3D registration,
point cloud registration
BibRef
Jiang, H.[Haobo],
Shen, Y.Q.[Ya-Qi],
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
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
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
Gálai, 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.
DOI Link
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 .