12.3.4.1.1 Register Point Cloud Data, Point Cloud Matching, Laser Scanner Data

TEASER++: Certifiable 3D Registration,

WWW Link. Code, Point Cloud Registration. 2002
A fast and robust point-cloud registration library. From the papers:
See also TEASER: Fast and Certifiable Point Cloud Registration.

Benjemaa, R.[Raouf], Schmitt, F.[Francis],
Fast global registration of 3D sampled surfaces using a multi-z-buffer technique,
IVC(17), No. 2, February 1999, pp. 113-123.
Elsevier DOI BibRef 9902
Earlier:
A Solution for the Registration of Multiple 3-D Point Sets Using Unit Quaternions,
ECCV98(II: 34).
Springer DOI BibRef
Earlier:
Fast Global Registration of 3D Sampled Surfaces Using a Mini-Buffer Technique,
3DIM97(4 - View Registration) 9702

See also Multi-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments. BibRef

Pottmann, H.[Helmut], Leopoldseder, S.[Stefan], Hofer, M.[Michael],
Registration without ICP,
CVIU(95), No. 1, July 2004, pp. 54-71.
Elsevier DOI 0407
BibRef
Earlier:
Simultaneous Registration of Multiple Views of a 3D Object,
PCV02(A: 265). 0305
Relies on instantaneous kinematics and on the geometry of the squared distance function of a surface.
See also On Surface Approximation Using Developable Surfaces. BibRef

Pottmann, H.[Helmut], Leopoldseder, S.[Stefan], Wallner, J.[Johannes], Peternell, M.[Martin],
Recognition and Reconstruction of Special Surfaces from Point Clouds,
PCV02(A: 271). 0305
BibRef

Hofer, M.[Manuel], Donoser, M.[Michael], Bischof, H.[Horst],
Semi-Global 3D Line Modeling for Incremental Structure-from-Motion,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Hofer, M.[Manuel], Wendel, A.[Andreas], Bischof, H.[Horst],
Incremental Line-based 3D Reconstruction using Geometric Constraints,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Hofer, M., Odehnal, B., Pottmann, H., Steiner, T., Wallner, J.,
3D Shape Recognition and Reconstruction Based on Line Element Geometry,
ICCV05(II: 1532-1538).
IEEE DOI 0510
BibRef

Pottmann, H.[Helmut], Wallner, J.[Johannes],
Freeform Architecture and Discrete Differential Geometry,
DGCI17(3-8).
Springer DOI 1711
BibRef

Pottmann, H.[Helmut], Hofer, M.[Michael], Odehnal, B.[Boris], Wallner, J.[Johannes],
Line Geometry for 3D Shape Understanding and Reconstruction,
ECCV04(Vol I: 297-309).
Springer DOI 0405
From surface normals, estimate shapes. BibRef

Diez, Y.[Yago], Martí, J.[Joan], Salvi, J.[Joaquim],
Hierarchical Normal Space Sampling to speed up point cloud coarse matching,
PRL(33), No. 16, 1 December 2012, pp. 2127-2133.
Elsevier DOI 1210
Coarse point cloud matching; Normal space sampling; Hierarchical algorithms; Data Structures BibRef

Roure, F.[Ferran], Lladó, X.[Xavier], Salvi, J.[Joaquim], Diez, Y.[Yago],
GridDS: a hybrid data structure for residue computation in point set matching,
MVA(30), No. 2, March 2019, pp. 291-307.
Springer DOI 1904
BibRef

Salvi, J.[Joaquim], Matabosch, C.[Carles], Fofi, D.[David], Forest, J.[Josep],
A review of recent range image registration methods with accuracy evaluation,
IVC(25), No. 5, 1 May 2007, pp. 578-596.
Elsevier DOI Survey, Range Registration. 0703
BibRef
Earlier: A2, A3, A1, A4:
Registration of Moving Surfaces by Means of One-Shot Laser Projection,
IbPRIA05(I:145).
Springer DOI 0509
3D reconstruction; Range image; Registration BibRef

Díez, Y.[Yago], Roure, F.[Ferran], Lladó, X.[Xavier], Salvi, J.[Joaquim],
A Qualitative Review on 3D Coarse Registration Methods,
Surveys(47), No. 3, April 2015, pp. Article No 45.
DOI Link 1506
Survey, Range Registration. 3D registration or matching is a crucial step in 3D model reconstruction. Registration applications span along a variety of research fields, including computational geometry, and geometric modeling. BibRef

Pribanic, T.[Tomislav], Diez, Y.[Yago], Roure, F.[Ferran], Salvi, J.[Joaquim],
An efficient surface registration using smartphone,
MVA(27), No. 4, May 2016, pp. 559-576.
Springer DOI 1605
BibRef

Basdogan, C.[Cagatay], Oztireli, A.C.[A. Cengiz],
A new feature-based method for robust and efficient rigid-body registration of overlapping point clouds,
VC(24), No. 7-9, July 2008, pp. xx-yy.
Springer DOI 0804
BibRef

Meng, Y.[Yu], Zhang, H.[Hui],
Registration of point clouds using sample-sphere and adaptive distance restriction,
VC(27), No. 6-8, June 2011, pp. 543-553.
WWW Link. 1107
BibRef

Schenk, S.[Stefan], Hanke, K.[Klaus],
Genetic Algorithms for Automatic Registration of Laser Scans with Imperfect and Subdivided Features (GAReg-ISF),
PFG(2009), No. 1, 2009, pp. 23-32.
WWW Link. 1211
BibRef
Earlier:
Combining genetic algorithms with imperfect and subdivided features for the automatic registration of point clouds (GAReg-ISF),
3DARCH09(xx-yy).
PDF File. 0902
BibRef

Mandow, A.[Anthony], Martinez, J.L.[Jorge L.], Reina, A.J.[Antonio J.], Morales, J.[Jesus],
Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration,
PRL(31), No. 11, 1 August 2010, pp. 1239-1250.
Elsevier DOI 1008
3D measurement system; Laser ranging; Point subsampling; Scene registration; Mobile robotics; Point matching BibRef

Martínez, J.L.[Jorge L.], Reina, A.J.[Antonio J.], Mandow, A.[Anthony], Morales, J.[Jesús],
3D registration of laser range scenes by coincidence of coarse binary cubes,
MVA(23), No. 5, September 2012, pp. 857-867.
WWW Link. 1208
BibRef

Muhle, D.[Daniel], Abraham, S.[Steffen], Wiggenhagen, M.[Manfred], Heipke, C.[Christian],
Identifying Correspondences in Sparse and Varying 3D Point Clouds using Distinctive Features,
PFG(2012), No. 5, 2012, pp. 535-546.
WWW Link. 1211
BibRef

Mateo, X.[Xavier], Orriols, X., Binefa, X.[Xavier],
Bayesian perspective for the registration of multiple 3D views,
CVIU(118), No. 1, 2014, pp. 84-96.
Elsevier DOI 1312
BibRef
Earlier: A1, A3, Only:
Plane Filtering for the Registration of Urban Range Laser Imagery,
IbPRIA09(136-143).
Springer DOI 0906
3D registration BibRef

Cirujeda, P.[Pol], Mateo, X.[Xavier], Dicente, Y.[Yashin], Binefa, X.[Xavier],
MCOV: A Covariance Descriptor for Fusion of Texture and Shape Features in 3D Point Clouds,
3DV14(551-558)
IEEE DOI 1503
Covariance matrices BibRef

Cheng, L.[Liang], Wu, Y.[Yang], Tong, L.H.[Li-Hua], Chen, Y.M.[Yan-Ming], Li, M.C.[Man-Chun],
Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud,
RS(7), No. 10, 2015, pp. 13921.
DOI Link 1511
BibRef

Govindu, V.M., Pooja, A.,
On Averaging Multiview Relations for 3D Scan Registration,
IP(23), No. 3, March 2014, pp. 1289-1302.
IEEE DOI 1403
Lie algebras BibRef

Wang, Y.B.[Yong-Bo], Wang, Y.J.[Yun-Jia], Wu, K.[Kan], Yang, H.C.[Hua-Chao], Zhang, H.[Hua],
A dual quaternion-based, closed-form pairwise registration algorithm for point clouds,
PandRS(94), No. 1, 2014, pp. 63-69.
Elsevier DOI 1407
LiDAR BibRef

Weber, T., Hänsch, R., Hellwich, O.,
Automatic registration of unordered point clouds acquired by Kinect sensors using an overlap heuristic,
PandRS(102), No. 1, 2015, pp. 96-109.
Elsevier DOI 1503
Point cloud fusion BibRef

Ge, X.M.[Xu-Ming], Wunderlich, T.[Thomas],
Surface-based matching of 3D point clouds with variable coordinates in source and target system,
PandRS(111), No. 1, 2016, pp. 1-12.
Elsevier DOI 1601
3D surface matching BibRef

Ge, X.M.[Xu-Ming],
Non-rigid registration of 3D point clouds under isometric deformation,
PandRS(121), No. 1, 2016, pp. 192-202.
Elsevier DOI 1609
Point clouds BibRef

Ge, X.M.[Xu-Ming],
Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets,
PandRS(130), No. 1, 2017, pp. 344-357.
Elsevier DOI 1708
Coarse, registration BibRef

Amamra, A.[Abdenour], Aouf, N.[Nabil], Stuart, D.[Dowling], Richardson, M.[Mark],
A recursive robust filtering approach for 3D registration,
SIViP(10), No. 5, May 2016, pp. 835-842.
Springer DOI 1608
BibRef

Guo, H.[Hao], Zhu, D.[Dehai], Mordohai, P.[Philippos],
Correspondence estimation for non-rigid point clouds with automatic part discovery,
VC(32), No. 12, December 2016, pp. 1511-1524.
WWW Link. 1611
BibRef

Sun, J.H.[Jun-Hua], Zhang, J.[Jie], Zhang, G.J.[Guang-Jun],
An automatic 3D point cloud registration method based on regional curvature maps,
IVC(56), No. 1, 2016, pp. 49-58.
Elsevier DOI 1612
3D point cloud BibRef

Meng, T.W.[Ting Wei], Choi, G.P.T.[Gary Pui-Tung], Lui, L.M.[Lok Ming],
TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds,
SIIMS(9), No. 4, 2016, pp. 1922-1962.
DOI Link 1612
BibRef

Lui, L.M.[Lok Ming], Lam, K.C.[Ka Chun], Yau, S.T.[Shing-Tung], Gu, X.F.[Xian-Feng],
Teichmüller extremal mapping and its applications to landmark matching registration,
OnlineNovember 2012.
WWW Link. BibRef 1211

Guislain, M.[Maximilien], Digne, J.[Julie], Chaine, R.[Raphaëlle], Monnier, G.[Gilles],
Fine scale image registration in large-scale urban LIDAR point sets,
CVIU(157), No. 1, 2017, pp. 90-102.
Elsevier DOI 1704
Large scale point sets BibRef

Persad, R.A.[Ravi Ancil], Armenakis, C.[Costas],
Automatic co-registration of 3D multi-sensor point clouds,
PandRS(130), No. 1, 2017, pp. 162-186.
Elsevier DOI 1708
Keypoints BibRef

Lai, R.J.[Rong-Jie], Zhao, H.K.[Hong-Kai],
Multiscale Nonrigid Point Cloud Registration Using Rotation-Invariant Sliced-Wasserstein Distance via Laplace-Beltrami Eigenmap,
SIIMS(10), No. 2, 2017, pp. 449-483.
DOI Link 1708
BibRef

Xiang, R.[Rui], Lai, R.J.[Rong-Jie], Zhao, H.K.[Hong-Kai],
Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment,
CVPR20(9510-9519)
IEEE DOI 2008
Shape, Sparse matrices, Stochastic processes, Iterative methods, Kernel, Manifolds, Perturbation methods BibRef

Ma, Y.X.[Yan-Xin], Guo, Y.L.[Yu-Lan], Lei, Y.J.[Yin-Jie], Lu, M.[Min], Zhang, J.[Jun],
Efficient rotation estimation for 3D registration and global localization in structured point clouds,
IVC(67), No. 1, 2017, pp. 52-66.
Elsevier DOI 1710
Structured point clouds BibRef

Sanchez, J.[Julia], Denis, F.[Florence], Checchin, P.[Paul], Dupont, F.[Florent], Trassoudaine, L.[Laurent],
Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Luo, N.[Nan], Wang, Q.[Quan],
Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix,
IET-CV(12), No. 2, March 2018, pp. 220-232.
DOI Link 1804
BibRef

Altantsetseg, E.[Enkhbayar], Khorloo, O.[Oyundolgor], Konno, K.[Kouichi],
Rigid registration of noisy point clouds based on higher-dimensional error metrics,
VC(34), No. 6-8, June 2018, pp. 1021-1030.
WWW Link. 1806
BibRef

Navarrete, J.[Javier], Viejo, D.[Diego], Cazorla, M.[Miguel],
Compression and registration of 3D point clouds using GMMs,
PRL(110), 2018, pp. 8-15.
Elsevier DOI 1806
3D compression, 3D registration BibRef

Qin, N.N.[Nan-Nan], Hu, X.Y.[Xiang-Yun], Dai, H.M.[Heng-Ming],
Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition,
PandRS(143), 2018, pp. 205-212.
Elsevier DOI 1808
Deep learning, 3D scene recognition, ALS, Multi-view representation, Fusion network BibRef

Chen, L.[Lei], Kuang, W.[Wenyue], Fu, K.[Kun],
A resample strategy and artificial bee colony optimization-based 3d range imaging registration,
CVIU(175), 2018, pp. 44-51.
Elsevier DOI 1812
Range image registration, Low overlapping rate, Resample strategy, Artificial bee colony algorithm, Bionic intelligence optimization BibRef

Pribanic, T.[Tomislav], Petkovic, T.[Tomislav], Ðonlic, M.[Matea],
3D registration based on the direction sensor measurements,
PR(88), 2019, pp. 532-546.
Elsevier DOI 1901
3D rigid registration, 3D reconstruction, Smartphone, Tablet, Accelerometer, Magnetometer, Structured light pattern BibRef

Pu, C.[Can], Song, R.[Runzi], Tylecek, R.[Radim], Li, N.[Nanbo], Fisher, R.B.[Robert B.],
SDF-MAN: Semi-Supervised Disparity Fusion with Multi-Scale Adversarial Networks,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Pu, C., Fisher, R.B.,
UDFNET: Unsupervised Disparity Fusion with Adversarial Networks,
ICIP19(1765-1769)
IEEE DOI 1910
Disparity Fusion, Adversarial network, Unsupervised, Stereo-stereo fusion, Stereo-lidar fusion BibRef

Xu, Y.S.[Yu-Sheng], Boerner, R.[Richard], Yao, W.[Wei], Hoegner, L.[Ludwig], Stilla, U.[Uwe],
Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets,
PandRS(151), 2019, pp. 106-123.
Elsevier DOI 1904
Point cloud, Coarse registration, Voxelization, Planar surface, 4PCS, Urban scene BibRef

Zhang, X.F.[Xian-Feng], Gao, R.[Renqiang], Sun, Q.[Quan], Cheng, J.[Junyi],
An Automated Rectification Method for Unmanned Aerial Vehicle LiDAR Point Cloud Data Based on Laser Intensity,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Pujol-Miró, A.[Alba], Casas, J.R.[Josep R.], Ruiz-Hidalgo, J.[Javier],
Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks,
IVC(83-84), 2019, pp. 51-60.
Elsevier DOI 1904
Matching, Point cloud, Convolutional Neural Networks BibRef

Boerner, R.[Richard], Xu, Y.S.[Yu-Sheng], Baran, R.[Ramona], Steinbacher, F.[Frank], Hoegner, L.[Ludwig], Stilla, U.[Uwe],
Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Zhang, S.[Shuang], Wang, H.[Hua], Gao, J.G.[Jin-Gang], Xing, C.Q.[Chun-Qi],
Frequency domain point cloud registration based on the Fourier transform,
JVCIR(61), 2019, pp. 170-177.
Elsevier DOI 1906
Fourier transform, Point cloud data, Frequency domain, Registration BibRef

Young, M.[Matthew], Pretty, C.[Chris], Agostinho, S.[Sérgio], Green, R.[Richard], Chen, X.Q.[Xiao-Qi],
Loss of Significance and Its Effect on Point Normal Orientation and Cloud Registration,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Yu, J.[Jie], Lin, Y.[Yi], Wang, B.[Bin], Ye, Q.[Qin], Cai, J.Q.[Jian-Qing],
An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point Clouds Registration,
GeoRS(57), No. 7, July 2019, pp. 4789-4798.
IEEE DOI 1907
Solid modeling, Feature extraction, Mathematical model, Data models, Parameter estimation, Convergence, total least squares BibRef

Zhao, B.[Bao], Chen, X.B.[Xiao-Bo], Le, X.Y.[Xin-Yi], Xi, J.T.[Jun-Tong],
A quantitative evaluation of comprehensive 3D local descriptors generated with spatial and geometrical features,
CVIU(190), 2020, pp. 102842.
Elsevier DOI 1911
Local feature descriptor, Local reference axis, Local reference frame, Object recognition, 3D registration BibRef

Bao, Z.S.[Zhen-Shan], Li, B.[Bowen], Zhang, W.B.[Wen-Bo],
Robustness of ToF and stereo fusion for high-accuracy depth map,
IET-CV(13), No. 7, Octomber 2019, pp. 676-681.
DOI Link 1911
BibRef

Prokop, M.[Miloš], Shaikh, S.A.[Salman Ahmed], Kim, K.S.[Kyoung-Sook],
Low Overlapping Point Cloud Registration Using Line Features Detection,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Chang, S.G.[Seung-Gyu], Ahn, C.[Chanho], Lee, M.[Minsik], Oh, S.H.[Song-Hwai],
Graph-matching-based correspondence search for nonrigid point cloud registration,
CVIU(192), 2020, pp. 102899.
Elsevier DOI 2002
Nonrigid registration, Graph matching, Point cloud, Mesh BibRef

Moyou, M., Rangarajan, A., Corring, J., Peter, A.M.,
A Grassmannian Graph Approach to Affine Invariant Feature Matching,
IP(29), 2020, pp. 3374-3387.
IEEE DOI 2002
Shape matching, 2D and 3D point registration, affine invariance, invariant coordinates, Grassmann manifold, object recognition BibRef

Chang, W.C.[Wen-Chung], Wu, C.H.[Chia-Hung],
Candidate-based matching of 3-D point clouds with axially switching pose estimation,
VC(36), No. 3, March 2020, pp. 593-607.
WWW Link. 2002
BibRef

Yang, H.[Heng], Shi, J.N.[Jing-Nan], Carlone, L.[Luca],
TEASER: Fast and Certifiable Point Cloud Registration,
To Appear
WWW Link. 2002
BibRef
Earlier: A1, A3, Only:
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates,
CRA19
WWW Link. BibRef

Chen, S., Nan, L., Xia, R., Zhao, J., Wonka, P.,
PLADE: A Plane-Based Descriptor for Point Cloud Registration With Small Overlap,
GeoRS(58), No. 4, April 2020, pp. 2530-2540.
IEEE DOI 2004
Data set, descriptor, point cloud, registration, scanning BibRef

Jia, X.[Xin], Yang, S.R.[Shou-Rui], Peng, Y.X.[Yu-Xin], Zhang, J.C.[Jun-Chao], Chen, S.Y.[Sheng-Yong],
DV-Net: Dual-view network for 3D reconstruction by fusing multiple sets of gated control point clouds,
PRL(131), 2020, pp. 376-382.
Elsevier DOI 2004
3D reconstruction, Deep learning, Point cloud fusion, Multiple views BibRef

Buján, S.[Sandra], Cordero, M.[Miguel], Miranda, D.[David],
Hybrid Overlap Filter for LiDAR Point Clouds Using Free Software,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Li, W.[Wei], Wang, C.[Cheng], Lin, C.[Congren], Xiao, G.[Guobao], Wen, C.[Chenglu], Li, J.[Jonathan],
Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization,
PandRS(163), 2020, pp. 284-299.
Elsevier DOI 2005
Supervoxel segmentation, Non-cooperative game, Keypoint correspondences, Point cloud registration BibRef

Wang, C.[Chen], Jiang, Y.X.[Yu-Xi], Wang, M.N.[Man-Ning],
Fast correspondence-based point cloud registration by pair-wise inlier checking and transformation decomposition,
PRL(135), 2020, pp. 418-424.
Elsevier DOI 2006
Point cloud registration, Correspondence, Potential inlier selection, Transformation decomposition BibRef

Gopalakrishnan, R.[Ranjith], Ali-Sisto, D.[Daniela], Kukkonen, M.[Mikko], Savolainen, P.[Pekka], Packalen, P.[Petteri],
Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Zhou, R.[Ruqin], Jiang, W.[Wanshou],
A Ridgeline-Based Terrain Co-Registration for Satellite LiDAR Point Clouds in Rough Areas,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Li, J.Y.[Jia-Yuan], Hu, Q.W.[Qing-Wu], Ai, M.Y.[Ming-Yao],
GESAC: Robust graph enhanced sample consensus for point cloud registration,
PandRS(167), 2020, pp. 363-374.
Elsevier DOI 2008
Point cloud registration, Coarse registration, Feature correspondence, RANSAC, Robust cost BibRef

Chaudhury, A.,
Multilevel Optimization for Registration of Deformable Point Clouds,
IP(29), 2020, pp. 8735-8746.
IEEE DOI 2009
Strain, Geometry, Shape, Deformable models, Optimization, expectation maximization BibRef

Quan, S., Yang, J.,
Compatibility-Guided Sampling Consensus for 3-D Point Cloud Registration,
GeoRS(58), No. 10, October 2020, pp. 7380-7392.
IEEE DOI 2009
Pose estimation, Task analysis, Feature extraction, Robustness, Measurement, transformation estimation BibRef

Fotsing, C.[Cedrique], Nziengam, N.[Nafissetou], Bobda, C.[Christophe],
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Zhang, Y.[Yuhe], Li, C.H.[Chun-Hui], Guo, B.[Bao], Guo, C.H.[Chen-Hao], Zhang, S.[Shunli],
KDD: A kernel density based descriptor for 3D point clouds,
PR(111), 2021, pp. 107691.
Elsevier DOI 2012
3D feature descriptor, Kernel density estimation, Point cloud registration, KL divergence BibRef

Zou, X.Y.[Xu-Yan], He, H.W.[Han-Wu], Wu, Y.M.[Yue-Ming], Chen, Y.B.[You-Bin], Xu, M.X.[Ming-Xi],
Automatic 3D point cloud registration algorithm based on triangle similarity ratio consistency,
IET-IPR(14), No. 14, December 2020, pp. 3314-3323
DOI Link 2012
BibRef

Huang, R.[Rong], Xu, Y.S.[Yu-Sheng], Yao, W.[Wei], Hoegner, L.[Ludwig], Stilla, U.[Uwe],
Robust global registration of point clouds by closed-form solution in the frequency domain,
PandRS(171), 2021, pp. 310-329.
Elsevier DOI 2012
Point cloud registration, Fourier transforms, Multidimensional phase correlation, Low-frequency components, Robust estimation BibRef

Toschi, I., Farella, E.M., Welponer, M., Remondino, F.,
Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds,
PandRS(172), 2021, pp. 160-170.
Elsevier DOI 2101
Registration, Aerial images, Airborne laser scanning, Quality evaluation, Dense image matching, Data fusion BibRef

Zampogiannis, K.[Konstantinos], Fermüller, C.[Cornelia], Aloimonos, Y.F.[Yi-Fannis],
Topology-Aware Non-Rigid Point Cloud Registration,
PAMI(43), No. 3, March 2021, pp. 1056-1069.
IEEE DOI 2102
Topology, Dynamics, Motion estimation, Geometry, Estimation, Image reconstruction, Non-rigid registration, dynamic topology BibRef

Kuçak, R.A.[Ramazan Alper], Erol, S.[Serdar], Erol, B.[Bihter],
An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Bolkas, D.[Dimitrios], Walton, G.[Gabriel], Kromer, R.[Ryan], Sichler, T.[Timothy],
Registration of multi-platform point clouds using edge detection for rockfall monitoring,
PandRS(175), 2021, pp. 366-385.
Elsevier DOI 2105
Point clouds, Registration, UAS, Laser scanning, Monitoring, Multi-scale transform BibRef

Stanley, M.H.[Michael H.], Laefer, D.F.[Debra F.],
Metrics for aerial, urban lidar point clouds,
PandRS(175), 2021, pp. 268-281.
Elsevier DOI 2105
Remote sensing, LiDAR, urban aerial laser scanning, LiDAR density, LiDAR accuracy, registration error BibRef

Yu, D.[Deng], Li, L.[Lei], Zheng, Y.Y.[You-Yi], Lau, M.[Manfred], Song, Y.Z.[Yi-Zhe], Tai, C.L.[Chiew-Lan], Fu, H.B.[Hong-Bo],
SketchDesc: Learning Local Sketch Descriptors for Multi-View Correspondence,
CirSysVideo(31), No. 5, 2021, pp. 1738-1750.
IEEE DOI 2105
BibRef

Li, L.[Lei], Zhu, S.Y.[Si-Yu], Fu, H.B.[Hong-Bo], Tan, P.[Ping], Tai, C.L.[Chiew-Lan],
End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds,
CVPR20(1916-1925)
IEEE DOI 2008
Rendering (computer graphics), Feature extraction, Neural networks, Shape, Geometry, Pipelines BibRef

Zhou, L.[Lei], Zhu, S.Y.[Si-Yu], Luo, Z.X.[Zi-Xin], Shen, T.W.[Tian-Wei], Zhang, R.[Runze], Zhen, M.M.[Ming-Min], Fang, T.[Tian], Quan, L.[Long],
Learning and Matching Multi-View Descriptors for Registration of Point Clouds,
ECCV18(XV: 527-544).
Springer DOI 1810
BibRef

Li, S.M.[Shi-Ming], Ge, X.M.[Xu-Ming], Li, S.F.[Sheng-Fu], Xu, B.[Bo], Wang, Z.D.[Zhen-Dong],
Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Wang, Y.B.[Yong-Bo], Zheng, N.S.[Nan-Shan], Bian, Z.F.[Zheng-Fu],
A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Wang, Y.[Yongbo], Zheng, N.[Nanshan], Bian, Z.[Zhengfu], Zhang, H.[Hua],
A Closed-Form Solution to Linear Feature-Based Registration of LiDAR Point Clouds,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, S.K.[Shi-Kun], Lu, R.D.[Ruo-Dan], Liu, J.Y.[Jian-Ya], Guo, L.[Liang],
Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Wang, F.Y.[Fei-Yu], Li, W.[Wen], Xu, D.[Dong],
Cross-Dataset Point Cloud Recognition Using Deep-Shallow Domain Adaptation Network,
IP(30), 2021, pp. 7364-7377.
IEEE DOI 2109
Task analysis, Feature extraction, Adaptation models, Image recognition, Target recognition, Training, transfer learning BibRef

Wang, Z.C.[Zi-Cheng], Li, W.[Wen], Xu, D.[Dong],
Domain Adaptive Sampling for Cross-Domain Point Cloud Recognition,
CirSysVideo(33), No. 12, December 2023, pp. 7604-7615.
IEEE DOI 2312
BibRef

Li, J.[Jian], Huang, S.W.[Shuo-Wen], Cui, H.[Hao], Ma, Y.R.[Yu-Rong], Chen, X.L.[Xiao-Long],
Automatic Point Cloud Registration for Large Outdoor Scenes Using a Priori Semantic Information,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef
And: RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Liu, W.B.[Wen-Bo], Sun, W.[Wei], Wang, S.[Shuxuan], Liu, Y.[Yi],
Coarse registration of point clouds with low overlap rate on feature regions,
SP:IC(98), 2021, pp. 116428.
Elsevier DOI 2109
Feature extraction, Low overlap rate, Point cloud registration, 4-points congruent sets BibRef

Wang, H.F.[Hua-Feng], Zhang, Y.M.[Ya-Ming], Liu, W.Q.[Wan-Quan], Gu, X.F.[Xian-Feng], Jing, X.[Xin], Liu, Z.C.[Zi-Cheng],
A novel GCN-based point cloud classification model robust to pose variances,
PR(121), 2022, pp. 108251.
Elsevier DOI 2109
Point cloud, Pose robust, Graph convolutional network, Classification BibRef

Zhou, R.[Ruqin], Li, X.X.[Xi-Xing], Jiang, W.S.[Wan-Shou],
SCANet: A Spatial and Channel Attention based Network for Partial-to-Partial Point Cloud Registration,
PRL(151), 2021, pp. 120-126.
Elsevier DOI 2110
BibRef

Wang, K.K.[Kang-Kan], Zhang, G.F.[Guo-Feng], Zheng, H.Y.[Hua-Yu], Yang, J.[Jian],
Learning Dense Correspondences for Non-Rigid Point Clouds With Two-Stage Regression,
IP(30), 2021, pp. 8468-8482.
IEEE DOI 2110
Estimation, Solid modeling, Shape, Predictive models, Deep learning, Data models, weak supervision BibRef

Li, J.Y.[Jia-Yuan], Hu, Q.W.[Qing-Wu], Ai, M.Y.[Ming-Yao],
Point Cloud Registration Based on One-Point RANSAC and Scale-Annealing Biweight Estimation,
GeoRS(59), No. 11, November 2021, pp. 9716-9729.
IEEE DOI 2111
Estimation, Robustness, Laser radar, Feature extraction, Detectors, Shape, Biweight estimator, random sample consensus (RANSAC) BibRef

Zhao, G.P.[Gen-Ping], Zhang, W.G.[Wei-Guang], Peng, Y.[Yeping], Wu, H.[Heng], Wang, Z.W.[Zhuo-Wei], Cheng, L.L.[Liang-Lun],
PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ge, X.M.[Xu-Ming], Zhu, Q.[Qing], Huang, L.[Lei], Li, S.F.[Sheng-Fu], Li, S.M.[Shi-Ming],
Global Registration of Multiview Unordered Forest Point Clouds Guided by Common Subgraphs,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Forestry, Vegetation, Registers, Reliability engineering, Merging, Germanium, Common subgraphs, tree-trunk BibRef

Yang, J.Q.[Jia-Qi], Huang, Z.Q.[Zhi-Qiang], Quan, S.[Siwen], Qi, Z.S.[Zhao-Shuai], Zhang, Y.N.[Yan-Ning],
SAC-COT: Sample Consensus by Sampling Compatibility Triangles in Graphs for 3-D Point Cloud Registration,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Pose estimation, Sampling methods, Robustness, Deep learning, Gaussian noise, Clutter, 3-D point cloud, sample consensus BibRef

Song, Y.[Yanan], Shen, W.M.[Wei-Ming], Lu, P.[Peng],
A novel partial-to-partial registration method based on sampling network,
JVCIR(82), 2022, pp. 103411.
Elsevier DOI 2201
Point cloud registration, Partial correspondence, Sampling network, Deep learning BibRef

Cong, Y.Z.[Yang-Zi], Chen, C.[Chi], Yang, B.S.[Bi-Sheng], Li, J.P.[Jian-Ping], Wu, W.T.[Wei-Tong], Li, Y.H.[Yu-Hao], Yang, Y.D.[Yan-Di],
3D-CSTM: A 3D continuous spatio-temporal mapping method,
PandRS(186), 2022, pp. 232-245.
Elsevier DOI 2203
Continuous 3D Mapping, Structural features, LiDAR, B-Spline BibRef

Kadam, P.[Pranav], Zhang, M.[Min], Liu, S.[Shan], Kuo, C.C.J.[C.C. Jay],
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method,
IP(31), 2022, pp. 2710-2725.
IEEE DOI 2204
Point cloud compression, Feature extraction, Task analysis, Transforms, Deep learning, Principal component analysis, 3D feature descriptor BibRef

Li, J.Y.[Jia-Yuan],
A Practical O(N^2) Outlier Removal Method for Correspondence-Based Point Cloud Registration,
PAMI(44), No. 8, August 2022, pp. 3926-3939.
IEEE DOI 2207
Upper bound, Feature extraction, Estimation, Time complexity, Detectors, Point cloud registration, outlier removal, O(N^2) running time BibRef

Li, J.Y.[Jia-Yuan], Shi, P.C.[Peng-Cheng], Hu, Q.W.[Qing-Wu], Zhang, Y.J.[Yong-Jun],
QGORE: Quadratic-Time Guaranteed Outlier Removal for Point Cloud Registration,
PAMI(45), No. 9, September 2023, pp. 11136-11151.
IEEE DOI 2309
BibRef

Nie, W.Z.[Wei-Zhi], Ke, Y.Q.[Yu-Qi], Zhao, Y.[Yue], Liang, Q.[Qi], Su, Y.T.[Yu-Ting],
LIMAN: Local Information-Based Multiattention Network for 3D Shape Recognition,
MultMedMag(29), No. 1, January 2022, pp. 65-73.
IEEE DOI 2205
Solid modeling, Feature extraction, Correlation, Visualization, Training data BibRef

Li, J.W.[Jian-Wei], Zhan, J.[Jiawang], Zhou, T.[Ting], Bento, V.A.[Virgílio A.], Wang, Q.F.[Qian-Feng],
Point cloud registration and localization based on voxel plane features,
PandRS(188), 2022, pp. 363-379.
Elsevier DOI 2205
Localization, Registration, Plane feature, Voxel, 3D point cloud BibRef

Arvanitis, G.[Gerasimos], Zacharaki, E.I.[Evangelia I.], Vása, L.[Libor], Moustakas, K.[Konstantinos],
Broad-to-Narrow Registration and Identification of 3D Objects in Partially Scanned and Cluttered Point Clouds,
MultMed(24), 2022, pp. 2230-2245.
IEEE DOI 2205
Feature extraction, Solid modeling, Shape, Object recognition, Histograms, Data models, cluttered scene BibRef

Zhang, Z.Y.[Zhi-Yuan], Sun, J.[Jiadai], Dai, Y.C.[Yu-Chao], Fan, B.[Bin], Liu, Q.[Qi],
Searching Dense Point Correspondences via Permutation Matrix Learning,
SPLetters(29), 2022, pp. 1192-1196.
IEEE DOI 2206
Point cloud compression, Shape, Estimation, Feature extraction, Transformers, Task analysis, end-to-end learning BibRef

Wang, Z.W.[Zi-Wei], Yan, S.J.[Si-Jie], Wu, L.[Long], Zhang, X.J.[Xiao-Jian], Chen, B.J.[Bin-Jiang],
Robust point clouds registration with point-to-point lp distance constraints in large-scale metrology,
PandRS(189), 2022, pp. 23-35.
Elsevier DOI 2206
Large-scale metrology, Point clouds registration, Featureless point clouds, norm constraints BibRef

Brun, A.[Aurélien], Cucci, D.A.[Davide A.], Skaloud, J.[Jan],
Lidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks,
PandRS(189), 2022, pp. 185-200.
Elsevier DOI 2206
Lidar, Georeferencing, Point cloud registration, UAVs, Pose-graph optimization, Dynamic networks BibRef

Li, S.K.[Shi-Kun], Ye, Y.[Yang], Liu, J.Y.[Jian-Ya], Guo, L.[Liang],
VPRNet: Virtual Points Registration Network for Partial-to-Partial Point Cloud Registration,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, Z.Y.[Zhi-Yuan], Sun, J.D.[Jia-Dai], Dai, Y.C.[Yu-Chao], Zhou, D.F.[Ding-Fu], Song, X.B.[Xi-Bin], He, M.Y.[Ming-Yi],
Self-supervised rigid transformation equivariance for accurate 3D point cloud registration,
PR(130), 2022, pp. 108784.
Elsevier DOI 2206
Point cloud, Rigid transformation equivariance, Learned cost volume BibRef

Zhang, Z.Y.[Zhi-Yuan], Sun, J.[Jiadai], Dai, Y.C.[Yu-Chao], Fan, B.[Bin], He, M.Y.[Ming-Yi],
VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration,
CirSysVideo(32), No. 8, August 2022, pp. 4997-5010.
IEEE DOI 2208
Point cloud compression, Reliability, Shape, Feature extraction, Geometry, Task analysis, Point cloud registration, hybrid loss function BibRef

Ghorbani, F.[Fariborz], Ebadi, H.[Hamid], Pfeifer, N.[Norbert], Sedaghat, A.[Amin],
Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Huang, H.[Hong], Ye, Z.[Zehao], Zhang, C.[Cheng], Yue, Y.[Yong], Cui, C.[Chunyi], Hammad, A.[Amin],
Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Zhao, L.[Lidu], Xiang, Z.F.[Zhong-Fu], Chen, M.L.[Mao-Lin], Ma, X.[Xiaping], Zhou, Y.[Yin], Zhang, S.C.[Shuang-Cheng], Hu, C.[Chuan], Hu, K.X.[Kai-Xin],
Establishment and Extension of a Fast Descriptor for Point Cloud Registration,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Yu, J.J.[Jin-Jin], Zhang, F.H.[Feng-Hao], Chen, Z.[Zhi], Liu, L.M.[Li-Man],
MSPR-Net: A Multi-Scale Features Based Point Cloud Registration Network,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Wang, X.[Xin], Ding, H.[Hui], Zhao, G.W.[Guang-Wei], Peng, Y.X.[Ya-Xin], Shen, C.M.[Chao-Min],
Scale robust point matching-Net: End-to-end scale point matching using Lie group,
IET-CV(16), No. 7, 2022, pp. 655-666.
DOI Link 2210
BibRef

Huang, X.S.[Xiao-Shui], Wang, Y.F.[Yang-Fu], Li, S.[Sheng], Mei, G.F.[Guo-Feng], Xu, Z.Y.[Zong-Yi], Wang, Y.C.[Yu-Cheng], Zhang, J.[Jian], Bennamoun, M.[Mohammed],
Robust real-world point cloud registration by inlier detection,
CVIU(224), 2022, pp. 103556.
Elsevier DOI 2211
Point cloud registration, Matching, Localization, 3d reconstruction BibRef

Jia, X.[Xin], Yang, S.[Shourui], Wang, Y.[Yunbo], Zhang, J.H.[Jian-Hua], Peng, Y.X.[Yu-Xin], Chen, S.Y.[Sheng-Yong],
Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions,
IP(31), 2022, pp. 6831-6846.
IEEE DOI 2212
Point cloud compression, Shape, Transformers, Image reconstruction, Solid modeling, Task analysis, Multi-view 3D reconstruction, dependency BibRef

Wang, J.T.[Jing-Tao], Yang, C.[Changcai], Wei, L.F.[Li-Fang], Chen, R.[Riqing],
CSCE-Net: Channel-Spatial Contextual Enhancement Network for Robust Point Cloud Registration,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Marcon, M.[Marlon], Spezialetti, R.[Riccardo], Salti, S.[Samuele], Silva, L.[Luciano], di Stefano, L.[Luigi],
Unsupervised Learning of Local Equivariant Descriptors for Point Clouds,
PAMI(44), No. 12, December 2022, pp. 9687-9702.
IEEE DOI 2212
Deep learning, Decoding, Computer architecture, Training, Training data, Shape, Deep learning on point clouds, registration BibRef

Wang, Y.J.[Yu-Jie], Yan, C.G.[Cheng-Gang], Feng, Y.T.[Yu-Tong], Du, S.Y.[Shao-Yi], Dai, Q.H.[Qiong-Hai], Gao, Y.[Yue],
STORM: Structure-Based Overlap Matching for Partial Point Cloud Registration,
PAMI(45), No. 1, January 2023, pp. 1135-1149.
IEEE DOI 2212
Point cloud compression, Feature extraction, Storms, Prediction algorithms, Shape, Pipelines, Point cloud registration, point cloud sampling BibRef

Zhao, M.Y.[Ming-Yang], Ma, L.[Lei], Jia, X.H.[Xiao-Hong], Yan, D.M.[Dong-Ming], Huang, T.J.[Tie-Jun],
GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing,
IP(31), 2022, pp. 7449-7464.
IEEE DOI 2212
Point cloud compression, Surface treatment, Probabilistic logic, Geometry, Simulated annealing, Robustness, simulated annealing BibRef

Zhang, Z.Y.[Zhi-Yuan], Dai, Y.C.[Yu-Chao], Fan, B.[Bin], Sun, J.[Jiadai], He, M.Y.[Ming-Yi],
Learning a Task-Specific Descriptor for Robust Matching of 3D Point Clouds,
CirSysVideo(32), No. 12, December 2022, pp. 8462-8475.
IEEE DOI 2212
Point cloud compression, Convolutional neural networks, Task analysis, Geometry, Transformers, Feature extraction, dynamic fusion module BibRef

Huang, J.H.[Jia-Hui], Birdal, T.[Tolga], Gojcic, Z.[Zan], Guibas, L.J.[Leonidas J.], Hu, S.M.[Shi-Min],
Multiway Non-Rigid Point Cloud Registration via Learned Functional Map Synchronization,
PAMI(45), No. 2, February 2023, pp. 2038-2053.
IEEE DOI 2301
Point cloud compression, Synchronization, Shape, Strain, Task analysis, Optimization, 3D point cloud, functional map synchronization BibRef

Poiesi, F.[Fabio], Boscaini, D.[Davide],
Learning General and Distinctive 3D Local Deep Descriptors for Point Cloud Registration,
PAMI(45), No. 3, March 2023, pp. 3979-3985.
IEEE DOI 2302
Point cloud compression, Histograms, Electronics packaging, Training, Covariance matrices, Aggregates, contrastive learning BibRef

Lv, C.L.[Chen-Lei], Lin, W.S.[Wei-Si], Zhao, B.Q.[Bao-Quan],
Intrinsic and Isotropic Resampling for 3D Point Clouds,
PAMI(45), No. 3, March 2023, pp. 3274-3291.
IEEE DOI 2302
Point cloud compression, Optimization, Level measurement, Surface fitting, Costs, Shape, Isotropic resampling, shape registration BibRef

Li, Q.[Qing], Wang, C.[Cheng], Wen, C.[Chenglu], Li, X.[Xin],
DeepSIR: Deep semantic iterative registration for LiDAR point clouds,
PR(137), 2023, pp. 109306.
Elsevier DOI 2302
Feature learning, 3D registration, LiDAR point clouds, Point score, Semantic segmentation BibRef

Monji-Azad, S.[Sara], Hesser, J.[Jürgen], Löw, N.[Nikolas],
A review of non-rigid transformations and learning-based 3D point cloud registration methods,
PandRS(196), 2023, pp. 58-72.
Elsevier DOI 2302
Point cloud registration, Non-rigid transformation, Quantitative assessments metrics, Robustness, Registration datasets BibRef

Bash, E.A.[Eleanor A.], Wecker, L.[Lakin], Rahman, M.M.[Mir Mustafizur], Dow, C.F.[Christine F.], McDermid, G.[Greg], Samavati, F.F.[Faramarz F.], Whitehead, K.[Ken], Moorman, B.J.[Brian J.], Medrzycka, D.[Dorota], Copland, L.[Luke],
A Multi-Resolution Approach to Point Cloud Registration without Control Points,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Lv, C.[Chenlei], Lin, W.S.[Wei-Si], Zhao, B.Q.[Bao-Quan],
KSS-ICP: Point Cloud Registration Based on Kendall Shape Space,
IP(32), 2023, pp. 1681-1693.
IEEE DOI 2303
Point cloud compression, Shape, Deep learning, Training, Manifolds, Task analysis, Kendall shape space, point cloud registration BibRef

Ren, S.[Siyu], Zeng, Y.M.[Yi-Ming], Hou, J.H.[Jun-Hui], Chen, X.D.[Xiao-Dong],
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence,
CirSysVideo(33), No. 3, March 2023, pp. 1198-1208.
IEEE DOI 2303
Point cloud compression, Feature extraction, Cameras, Detectors, Feeds, Visualization, Point cloud, registration, cross-modality, deep learning BibRef

Zhang, Y.X.[Yu-Xin], Sun, Z.L.[Zhan-Li], Zeng, Z.G.[Zhi-Gang], Lam, K.M.[Kin-Man],
Point Cloud Registration Using Multiattention Mechanism and Deep Hybrid Features,
IEEE_Int_Sys(38), No. 1, January 2023, pp. 58-68.
IEEE DOI 2303
Point cloud compression, Feature extraction, Task analysis, Intelligent systems, Convolution, Sun, point cloud registration, deep hybrid feature BibRef

Wu, Y.[Yue], Zhang, Y.[Yue], Fan, X.L.[Xiao-Long], Gong, M.[Maoguo], Miao, Q.G.[Qi-Guang], Ma, W.P.[Wen-Ping],
INENet: Inliers Estimation Network With Similarity Learning for Partial Overlapping Registration,
CirSysVideo(33), No. 3, March 2023, pp. 1413-1426.
IEEE DOI 2303
Point cloud compression, Feature extraction, Estimation, Prediction algorithms, Probability, Deep learning, Transforms, partial overlap registration BibRef

Fu, K.X.[Ke-Xue], Luo, J.Z.[Jia-Zheng], Luo, X.Y.[Xiao-Yuan], Liu, S.L.[Shao-Lei], Zhang, C.X.[Chen-Xi], Wang, M.N.[Man-Ning],
Robust Point Cloud Registration Framework Based on Deep Graph Matching,
PAMI(45), No. 5, May 2023, pp. 6183-6195.
IEEE DOI 2304
BibRef
Earlier: A1, A4, A3, A6, Only: CVPR21(8889-8898)
IEEE DOI 2111
Point cloud compression, Feature extraction, Neural networks, Transformers, Prediction algorithms, Geometry, Deep learning, point cloud registration. Transforms, Robot sensing systems, Topology BibRef

Yuan, M.Z.[Ming-Zhi], Li, Z.H.[Zhi-Hao], Jin, Q.Y.[Qiu-Ye], Chen, X.R.[Xin-Rong], Wang, M.N.[Man-Ning],
PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration,
ECCV22(IX:595-611).
Springer DOI 2211
BibRef

Cheng, X.Y.[Xiao-Ya], Yan, S.[Shen], Liu, Y.[Yan], Zhang, M.[Maojun], Chen, C.[Chen],
R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision,
RS(15), No. 7, 2023, pp. 1889.
DOI Link 2304
BibRef

Zhao, Y.[Yang], Fan, L.[Lei],
Review on Deep Learning Algorithms and Benchmark Datasets for Pairwise Global Point Cloud Registration,
RS(15), No. 8, 2023, pp. 2060.
DOI Link 2305
BibRef

He, J.F.[Jian-Feng], Deng, J.C.[Jia-Cheng], Zhang, T.Z.[Tian-Zhu], Zhang, Z.[Zhe], Zhang, Y.D.[Yong-Dong],
Hierarchical Shape-Consistent Transformer for Unsupervised Point Cloud Shape Correspondence,
IP(32), 2023, pp. 2734-2748.
IEEE DOI 2305
Shape, Point cloud compression, Transformers, Feature extraction, Task analysis, Semantics, Shape correspondence, transformer, shape-consistent constraint BibRef

Yan, L.[Li], Wei, P.C.[Peng-Cheng], Xie, H.[Hong], Dai, J.C.[Ji-Cheng], Wu, H.[Hao], Huang, M.[Ming],
A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration,
PAMI(45), No. 7, July 2023, pp. 7986-8002.
IEEE DOI 2306
Point cloud compression, Reliability, Transmission line matrix methods, Task analysis, Histograms, reliability of graph BibRef

Li, J.W.[Jian-Wei], Huang, X.[Xin], Zhan, J.[Jiawang],
High-Precision Motion Detection and Tracking Based on Point Cloud Registration and Radius Search,
ITS(24), No. 6, June 2023, pp. 6322-6335.
IEEE DOI 2306
Tracking, Radar tracking, Point cloud compression, Motion detection, Target tracking, Sensors, Feature extraction, registration BibRef

Zhang, Y.[Yu], Zhang, W.H.[Wen-Hao], Li, J.L.[Jin-Long],
Partial-to-Partial Point Cloud Registration by Rotation Invariant Features and Spatial Geometric Consistency,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Qin, Z.[Zheng], Yu, H.[Hao], Wang, C.J.[Chang-Jian], Guo, Y.L.[Yu-Lan], Peng, Y.X.[Yu-Xing], Ilic, S.[Slobodan], Hu, D.[Dewen], Xu, K.[Kai],
GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer,
PAMI(45), No. 8, August 2023, pp. 9806-9821.
IEEE DOI 2307
BibRef
Earlier: A1, A2, A3, A4, A5, A8, Only:
Geometric Transformer for Fast and Robust Point Cloud Registration,
CVPR22(11133-11142)
IEEE DOI 2210
Point cloud compression, Transformers, Feature extraction, Benchmark testing, Convergence, Task analysis, transformer. Codes, Estimation, Encoding, Pose estimation and tracking, Scene analysis and understanding BibRef

Wang, C.J.[Cheng-Jun], Zheng, Z.[Zhen], Zha, B.T.[Bing-Ting], Li, H.J.[Hao-Jie],
Fast Robust Point Cloud Registration Based on Compatibility Graph and Accelerated Guided Sampling,
RS(16), No. 15, 2024, pp. 2789.
DOI Link 2408
BibRef

Chu, G.H.[Guang-Han], Fan, D.Z.[Da-Zhao], Dong, Y.[Yang], Ji, S.[Song], Gu, L.-.Y.[Lin--Yu], Li, D.Z.[Dong-Zi], Zhang, W.[Wu],
Robust registration of aerial and close-range photogrammetric point clouds using visual context features and scale consistency,
IET-IPR(17), No. 9, 2023, pp. 2698-2709.
DOI Link 2307
computer vision, image matching, image processing BibRef

Wu, X.[Xin], Wei, X.L.[Xiao-Long], Xu, H.J.[Hao-Jun], Li, C.Z.[Cai-Zhi], Hou, Y.H.[Yuan-Han], Yin, Y.Z.[Yi-Zhen], He, W.F.[Wei-Feng],
PointCNT: A One-Stage Point Cloud Registration Approach Based on Complex Network Theory,
RS(15), No. 14, 2023, pp. 3545.
DOI Link 2307
BibRef

Wu, Y.[Yue], Hu, X.[Xidao], Zhang, Y.[Yue], Gong, M.[Maoguo], Ma, W.P.[Wen-Ping], Miao, Q.G.[Qi-Guang],
SACF-Net: Skip-Attention Based Correspondence Filtering Network for Point Cloud Registration,
CirSysVideo(33), No. 8, August 2023, pp. 3585-3595.
IEEE DOI 2308
Feature extraction, Point cloud compression, Decoding, Task analysis, Estimation, Pipelines, Point cloud, partial overlap registration BibRef

Wang, X.[Xuchu], Yuan, Y.[Yue],
GCMTN: Low-Overlap Point Cloud Registration Network Combining Dense Graph Convolution and Multilevel Interactive Transformer,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Lu, F.[Fan], Chen, G.[Guang], Liu, Y.L.[Yin-Long], Zhan, Y.B.[Yi-Bing], Li, Z.J.[Zhi-Jun], Tao, D.C.[Da-Cheng], Jiang, C.J.[Chang-Jun],
Sparse-to-Dense Matching Network for Large-Scale LiDAR Point Cloud Registration,
PAMI(45), No. 9, September 2023, pp. 11270-11282.
IEEE DOI 2309
BibRef

Lu, F.[Fan], Chen, G.[Guang], Liu, Y.L.[Yin-Long], Zhang, L.J.[Li-Jun], Qu, S.Q.[San-Qing], Liu, S.[Shu], Gu, R.Q.[Rong-Qi], Jiang, C.J.[Chang-Jun],
HRegNet: A Hierarchical Network for Efficient and Accurate Outdoor LiDAR Point Cloud Registration,
PAMI(45), No. 10, October 2023, pp. 11884-11897.
IEEE DOI 2310
BibRef
Earlier: A1, A2, A3, A4, A5, A6, A7, Only:
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration,
ICCV21(15994-16003)
IEEE DOI 2203
Point cloud compression, Laser radar, Computer network reliability, Pipelines, Feature extraction, Vision applications and systems BibRef

Xue, W.Y.[Wei-Yi], Lu, F.[Fan], Chen, G.[Guang],
HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration,
WACV24(3381-3391)
IEEE DOI 2404
Point cloud compression, Laser radar, Computer network reliability, Pose estimation, Neural networks, 3D computer vision BibRef

Zhao, T.M.[Tian-Ming], Li, L.F.[Lin-Feng], Tian, T.[Tian], Ma, J.Y.[Jia-Yi], Tian, J.W.[Jin-Wen],
Patch-guided point matching for point cloud registration with low overlap,
PR(144), 2023, pp. 109876.
Elsevier DOI 2310
Point cloud registration, Low overlap, Matching pyramid, Cross-level fusion BibRef

Yao, R.Z.[Run-Zhao], Du, S.[Shaoyi], Cui, W.T.[Wen-Ting], Ye, A.[Aixue], Wen, F.[Feng], Zhang, H.B.[Hong-Bo], Tian, Z.Q.[Zhi-Qiang], Gao, Y.[Yue],
Hunter: Exploring High-Order Consistency for Point Cloud Registration With Severe Outliers,
PAMI(45), No. 12, December 2023, pp. 14760-14776.
IEEE DOI 2311
BibRef

Feng, Y.[Yong], Leung, K.L.[Ka Lun], Li, Y.K.[Ying-Kui], Wong, K.L.[Kwai Lam],
An AI-Based Workflow for Fast Registration of UAV-Produced 3D Point Clouds,
RS(15), No. 21, 2023, pp. 5163.
DOI Link 2311
BibRef

Glira, P.[Philipp], Weidinger, C.[Christoph], Otepka-Schremmer, J.[Johannes], Ressl, C.[Camillo], Pfeifer, N.[Norbert], Haberler-Weber, M.[Michaela],
Nonrigid Point Cloud Registration Using Piecewise Tricubic Polynomials as Transformation Model,
RS(15), No. 22, 2023, pp. 5348.
DOI Link 2311
BibRef

Slimani, K.[Karim], Achard, C.[Catherine], Tamadazte, B.[Brahim],
RoCNet++: Triangle-based descriptor for accurate and robust point cloud registration,
PR(147), 2024, pp. 110108.
Elsevier DOI 2312
Point cloud learning, Registration, Geometric descriptor, Attention mechanism, Pose estimation BibRef

Chen, Y.[Yilin], Mei, Y.[Yang], Yu, B.C.[Bao-Cheng], Xu, W.X.[Wen-Xia], Wu, Y.Q.[Yi-Qi], Zhang, D.J.[De-Jun], Yan, X.H.[Xiao-Hu],
A Robust Multi-Local to Global with Outlier Filtering for Point Cloud Registration,
RS(15), No. 24, 2023, pp. 5641.
DOI Link 2401
BibRef

Cui, C.H.[Cheng-Hao], Liu, Y.L.[Yu-Ling], Zhang, F.[Fubo], Shi, M.[Minan], Chen, L.[Longyong], Li, W.J.[Wen-Jie], Li, Z.H.[Zhen-Hua],
A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes,
RS(16), No. 3, 2024, pp. 601.
DOI Link 2402
BibRef

Han, T.Y.[Tian-Yu], Zhang, R.J.[Rui-Jie], Kan, J.M.[Jiang-Ming], Dong, R.[Ruifang], Zhao, X.X.[Xi-Xuan], Yao, S.[Shun],
A Point Cloud Registration Framework with Color Information Integration,
RS(16), No. 5, 2024, pp. 743.
DOI Link 2403
BibRef

Xing, X.J.[Xue-Jun], Lu, Z.[Zhengda], Wang, Y.Q.[Yi-Qun], Xiao, J.[Jun],
Efficient Single Correspondence Voting for Point Cloud Registration,
IP(33), 2024, pp. 2116-2130.
IEEE DOI Code:
WWW Link. 2403
Point cloud compression, Iterative methods, Feature extraction, Deep learning, 3D point cloud BibRef

Zeng, T.J.[Tian-Jiao], Zhang, W.[Wensi], Zhan, X.[Xu], Xu, X.[Xiaowo], Liu, Z.Y.[Zi-Yang], Wang, B.Y.[Bao-You], Zhang, X.L.[Xiao-Ling],
A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception,
RS(16), No. 6, 2024, pp. 952.
DOI Link 2403
BibRef

Yu, H.[Hao], Hou, J.[Ji], Qin, Z.[Zheng], Saleh, M.[Mahdi], Shugurov, I.[Ivan], Wang, K.[Kai], Busam, B.[Benjamin], Ilic, S.[Slobodan],
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration,
PAMI(46), No. 5, May 2024, pp. 3796-3812.
IEEE DOI 2404
Point cloud compression, Geometry, Task analysis, Benchmark testing, Shape, Pattern analysis, coarse-to-fine correspondences BibRef

Lyu, M.J.[Meng-Jin], Yang, J.[Jie], Qi, Z.Q.[Zhi-Quan], Xu, R.J.[Rui-Jie], Liu, J.B.[Jia-Bin],
Rigid pairwise 3D point cloud registration: A survey,
PR(151), 2024, pp. 110408.
Elsevier DOI 2404
3D point cloud, Registration, Review, Deep learning BibRef

Xu, Z.Y.[Zong-Yi], Jiang, X.[Xinqi], Gao, X.Y.[Xin-Yu], Gao, R.[Rui], Gu, C.J.[Chang-Jun], Zhang, Q.[Qianni], Li, W.S.[Wei-Sheng], Gao, X.B.[Xin-Bo],
IGReg: Image-Geometry-Assisted Point Cloud Registration via Selective Correlation Fusion,
MultMed(26), 2024, pp. 7475-7489.
IEEE DOI 2405
Point cloud compression, Feature extraction, Correlation, Reliability, Geometry, Iterative methods, Low-geometry area, repetitive patterns BibRef

Wang, Y.[Yong], Zhou, P.[Pengbo], Geng, G.H.[Guo-Hua], An, L.[Li], Zhang, Q.[Qi],
Low-Overlap Point Cloud Registration With Transformer,
SPLetters(31), 2024, pp. 1469-1473.
IEEE DOI 2406
Point cloud compression, Encoding, Transformers, Heuristic algorithms, Noise, Adaptation models, low overlap BibRef

Yuan, M.[Munan], Li, X.[Xiru],
Point cloud registration method for indoor depth sensor acquisition system based on dual graph computation with irregular shape factors,
IET-IPR(18), No. 8, 2024, pp. 2161-2178.
DOI Link 2406
computer vision, image registration, learning (artificial intelligence) BibRef

Zhao, Y.F.[Ya-Fei], Chen, L.N.[Li-Neng], Zhou, Q.C.[Quan-Chen], Zuo, J.B.[Jia-Bao], Wang, H.[Huan], Ren, M.W.[Ming-Wu],
A Registration Method of Overlap Aware Point Clouds Based on Transformer-to-Transformer Regression,
RS(16), No. 11, 2024, pp. 1898.
DOI Link 2406
BibRef

Zhao, H.X.[Hai-Xia], Sun, J.Q.[Jia-Qi], Dong, B.[Bin],
DAMF-Net: Unsupervised Domain-Adaptive Multimodal Feature Fusion Method for Partial Point Cloud Registration,
RS(16), No. 11, 2024, pp. 1993.
DOI Link 2406
BibRef

Jia, S.[Shoujun], Liu, C.[Chun], Wu, H.[Hangbin], Huan, W.H.[Wei-Hua], Wang, S.[Shufan],
Incremental registration towards large-scale heterogeneous point clouds by hierarchical graph matching,
PandRS(213), 2024, pp. 87-106.
Elsevier DOI 2406
Point cloud, Incremental registration, Graph matching, Large-scale scene, Geometric heterogeneity BibRef

Ma, T.J.[Tian-Jiao], Han, G.L.[Guang-Liang], Chu, Y.Z.[Yong-Zhi], Ren, H.[Hong],
Sparse-to-Dense Point Cloud Registration Based on Rotation-Invariant Features,
RS(16), No. 13, 2024, pp. 2485.
DOI Link 2407
BibRef

Yuan, M.Z.[Ming-Zhi], Fu, K.[Kexue], Li, Z.H.[Zhi-Hao], Meng, Y.C.[Yu-Cong], Shen, A.[Ao], Wang, M.[Manning],
Robust Point Cloud Registration via Random Network Co-Ensemble,
CirSysVideo(34), No. 7, July 2024, pp. 5742-5752.
IEEE DOI Code:
WWW Link. 2407
Feature extraction, Point cloud compression, Training, Ensemble learning, Estimation, Kernel, Learning systems, random network BibRef

Jiang, Y.[Yinuo], Zhou, B.T.[Bei-Tong], Liu, X.Y.[Xiao-Yu], Li, Q.Y.[Qing-Yi], Cheng, C.[Cheng],
GTINet: Global Topology-Aware Interactions for Unsupervised Point Cloud Registration,
CirSysVideo(34), No. 7, July 2024, pp. 6363-6375.
IEEE DOI 2407
Point cloud compression, Feature extraction, Circuits and systems, Context modeling, Training, contextual interactions BibRef

Zhang, S.K.[Shi-Kun], Yang, J.Q.[Jia-Qi], Qi, Z.S.[Zhao-Shuai], Zhang, Y.N.[Yan-Ning],
Toward Meta-Shape-Based Multi-View 3D Point Cloud Registration: An Evaluation,
CirSysVideo(34), No. 7, July 2024, pp. 5361-5375.
IEEE DOI 2407
Point cloud compression, Shape, Optimization, Three-dimensional displays, Quaternions, Matrix decomposition, performance evaluation BibRef

Xiang, S.[Siyi], Guo, S.Y.[Shi-Yi], Wei, H.[Hao], Liu, B.X.[Bing-Xi], Zhang, D.[Dabo],
An Efficient Outlier Rejection Algorithm for Point Cloud Registration,
SPLetters(31), 2024, pp. 1775-1779.
IEEE DOI 2408
Point cloud compression, Feature extraction, Accuracy, Signal processing algorithms, Noise, Noise reduction, spatial consistency BibRef

Cao, F.L.[Fei-Long], Wang, L.P.[Ling-Peng], Ye, H.L.[Hai-Liang],
SharpGConv: A Novel Graph Method With Plug-and-Play Sharpening Convolution for Point Cloud Registration,
CirSysVideo(34), No. 8, August 2024, pp. 7095-7105.
IEEE DOI 2408
Point cloud compression, Feature extraction, Convolution, Transformers, Data mining, Circuits and systems, Deep learning, feature matching BibRef

Zhou, K.Y.[Ke-Yang], Bhatnagar, B.L.[Bharat Lal], Schiele, B.[Bernt], Pons-Moll, G.[Gerard],
Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes,
3DV22(1-11)
IEEE DOI 2408
Point cloud compression, Interpolation, Shape, Subspace constraints, Semantics, Neural networks BibRef

Efroni, O.[Omri], Ginzburg, D.[Dvir], Raviv, D.[Dan],
Spectral Teacher for a Spatial Student: Spectrum-Aware Real-Time Dense Shape Correspondence,
3DV22(1-10)
IEEE DOI 2408
Training, Codes, Shape, Databases, Real-time systems, Stability analysis, 3D Point Clouds, Non Rigid Shapes, Functional Space BibRef

Fischer, K.[Kai], Simon, M.[Martin], Milz, S.[Stefan], Mäder, P.[Patrick],
MagneticPillars: Efficient Point Cloud Registration through Hierarchized Birds-Eye-View Cell Correspondence Refinement,
WACV24(7371-7380)
IEEE DOI 2404
Point cloud compression, Runtime, Laser radar, Magnetic separation, Pose estimation, Optimization methods, Vectors, Applications, Robotics BibRef

Burgdorfer, N.[Nathaniel], Mordohai, P.[Philippos],
V-FUSE: Volumetric Depth Map Fusion with Long-Range Constraints,
ICCV23(3426-3435)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, Q.[Quan], Zhu, H.Z.[Hong-Zi], Zhou, Y.S.[Yun-Song], Li, H.Y.[Hong-Yang], Chang, S.[Shan], Guo, M.[Minyi],
Density-invariant Features for Distant Point Cloud Registration,
ICCV23(18169-18179)
IEEE DOI Code:
WWW Link. 2401
BibRef

Xing, X.Y.[Xiao-Yan], Groh, K.[Konrad], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Intrinsic Appearance Decomposition Using Point Cloud Representation,
CVMeta23(4234-4238)
IEEE DOI 2401
BibRef

Chen, G.Y.[Guang-Yan], Wang, M.L.[Mei-Ling], Yuan, L.[Li], Yang, Y.[Yi], Yue, Y.F.[Yu-Feng],
Rethinking Point Cloud Registration as Masking and Reconstruction,
ICCV23(17671-17681)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, J.M.[Jiu-Ming], Wang, G.M.[Guang-Ming], Liu, Z.[Zhe], Jiang, C.K.[Chao-Kang], Pollefeys, M.[Marc], Wang, H.S.[He-Sheng],
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration,
ICCV23(8417-8426)
IEEE DOI Code:
WWW Link. 2401
BibRef

Chen, S.[Suyi], Xu, H.[Hao], Li, R.[Ru], Liu, G.H.[Guang-Hui], Fu, C.W.[Chi-Wing], Liu, S.C.[Shuai-Cheng],
SIRA-PCR: Sim-to-Real Adaptation for 3D Point Cloud Registration,
ICCV23(14348-14359)
IEEE DOI Code:
WWW Link. 2401
BibRef

Hatem, A.[Ahmed], Qian, Y.M.[Yi-Ming], Wang, Y.[Yang],
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning,
ICCV23(16448-16458)
IEEE DOI 2401
BibRef

Dang, Z.[Zheng], Salzmann, M.[Mathieu],
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration,
ICCV23(8975-8985)
IEEE DOI 2401
BibRef

Huang, T.X.[Tian-Xin], Ding, Z.G.[Zhong-Gan], Zhang, J.N.[Jiang-Ning], Tai, Y.[Ying], Zhang, Z.Y.[Zhen-Yu], Chen, M.[Mingang], Wang, C.J.[Cheng-Jie], Liu, Y.[Yong],
Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space,
CVPR23(12208-12217)
IEEE DOI 2309
BibRef

Widdowson, D.[Daniel], Kurlin, V.[Vitaliy],
Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives,
CVPR23(1275-1284)
IEEE DOI 2309
BibRef

Mei, G.F.[Guo-Feng], Tang, H.[Hao], Huang, X.S.[Xiao-Shui], Wang, W.J.[Wei-Jie], Liu, J.[Juan], Zhang, J.[Jian], Van Gool, L.J.[Luc J.], Wu, Q.[Qiang],
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration,
CVPR23(13611-13620)
IEEE DOI 2309
BibRef

Zhang, X.[Xiyu], Yang, J.Q.[Jia-Qi], Zhang, S.K.[Shi-Kun], Zhang, Y.N.[Yan-Ning],
3D Registration with Maximal Cliques,
CVPR23(17745-17754)
IEEE DOI 2309
BibRef

Jiang, H.[Haobo], Dang, Z.[Zheng], Wei, Z.[Zhen], Xie, J.[Jin], Yang, J.[Jian], Salzmann, M.[Mathieu],
Robust Outlier Rejection for 3D Registration with Variational Bayes,
CVPR23(1148-1157)
IEEE DOI 2309
BibRef

Jiang, P.[Puhua], Sun, M.Z.[Ming-Ze], Huang, R.[Ruqi],
Neural Intrinsic Embedding for Non-Rigid Point Cloud Matching,
CVPR23(21835-21845)
IEEE DOI 2309
BibRef

Ao, S.[Sheng], Hu, Q.Y.[Qing-Yong], Wang, H.[Hanyun], Xu, K.[Kai], Guo, Y.L.[Yu-Lan],
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration,
CVPR23(1255-1264)
IEEE DOI 2309
BibRef

Wang, H.P.[Hai-Ping], Liu, Y.[Yuan], Dong, Z.[Zhen], Guo, Y.L.[Yu-Lan], Liu, Y.S.[Yu-Shen], Wang, W.P.[Wen-Ping], Yang, B.S.[Bi-Sheng],
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting,
CVPR23(9506-9515)
IEEE DOI 2309
BibRef

Yu, J.[Junle], Ren, L.[Luwei], Zhou, W.H.[Wen-Hui], Zhang, Y.[Yu], Lin, L.[Lili], Dai, G.J.[Guo-Jun],
PEAL: Prior-embedded Explicit Attention Learning for Low-overlap Point Cloud Registration,
CVPR23(17702-17711)
IEEE DOI 2309
BibRef

Yu, H.[Hao], Qin, Z.[Zheng], Hou, J.[Ji], Saleh, M.[Mahdi], Li, D.S.[Dong-Sheng], Busam, B.[Benjamin], Ilic, S.[Slobodan],
Rotation-Invariant Transformer for Point Cloud Matching,
CVPR23(5384-5393)
IEEE DOI 2309
BibRef

Qin, Z.[Zheng], Yu, H.[Hao], Wang, C.J.[Chang-Jian], Peng, Y.X.[Yu-Xing], Xu, K.[Kai],
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration,
CVPR23(5394-5403)
IEEE DOI 2309
BibRef

Deng, J.C.[Jia-Cheng], Wang, C.X.[Chu-Xin], Lu, J.H.[Jia-Hao], He, J.F.[Jian-Feng], Zhang, T.Z.[Tian-Zhu], Yu, J.[Jiyang], Zhang, Z.[Zhe],
SE-ORNet: Self-Ensembling Orientation-Aware Network for Unsupervised Point Cloud Shape Correspondence,
CVPR23(5364-5373)
IEEE DOI 2309
BibRef

Qiao, D.H.[Dong-Hao], Zulkernine, F.[Farhana],
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds,
WACV23(1186-1195)
IEEE DOI 2302
Point cloud compression, Adaptation models, Laser radar, Adaptive systems, Vehicle detection, Neural networks, Urban areas, Robotics BibRef

Mei, G.F.[Guo-Feng], Poiesi, F.[Fabio], Saltori, C.[Cristiano], Zhang, J.[Jian], Ricci, E.[Elisa], Sebe, N.[Nicu],
Overlap-guided Gaussian Mixture Models for Point Cloud Registration,
WACV23(4500-4509)
IEEE DOI 2302
Point cloud compression, Neural networks, Probabilistic logic, Transformers, Minimization, Algorithms: 3D computer vision BibRef

Flood, G.[Gabrielle], Tegler, E.[Erik], Gillsjö, D.[David], Heyden, A.[Anders], Åström, K.[Kalle],
Minimal Solvers for Point Cloud Matching with Statistical Deformations,
ICPR22(4196-4203)
IEEE DOI 2212
Point cloud compression, Simultaneous localization and mapping, Shape, Merging, Task analysis BibRef

Liu, X.Y.[Xing-Yu], Wang, G.[Gu], Li, Y.[Yi], Ji, X.Y.[Xiang-Yang],
CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement,
ECCV22(II:499-516).
Springer DOI 2211
BibRef

Ginzburg, D.[Dvir], Raviv, D.[Dan],
Deep Weighted Consensus Dense Correspondence Confidence Maps for 3d Shape Registration,
ICIP22(71-75)
IEEE DOI 2211
Point cloud compression, Shape, Pipelines, Iterative methods, Task analysis, Rigid alignment, Geometric deep learning, Robust optimization BibRef

Mei, G.F.[Guo-Feng], Huang, X.S.[Xiao-Shui], Zhang, J.[Jian], Wu, Q.[Qiang],
Partial Point Cloud Registration Via Soft Segmentation,
ICIP22(681-685)
IEEE DOI 2211
Point cloud compression, Degradation, Image segmentation, Partitioning algorithms, Registration, correspondences-free, partial overlapped BibRef

Bökman, G.[Georg], Kahla, F.[Fredrik], Flinth, A.[Axel],
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds,
CVPR22(10966-10975)
IEEE DOI 2210
Point cloud compression, Deep learning, Neural networks, Memory management, Estimation, Machine learning BibRef

El Banani, M.[Mohamed], Johnson, J.[Justin],
Bootstrap Your Own Correspondences,
ICCV21(6413-6422)
IEEE DOI 2203
Point cloud compression, Representation learning, Visualization, Scalability, Pipelines, Supervised learning, Stereo, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Choe, J.[Jaesung], Im, S.H.[Sung-Hoon], Rameau, F.[Francois], Kang, M.J.[Min-Jun], Kweon, I.S.[In So],
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction,
ICCV21(16066-16075)
IEEE DOI 2203
Deep learning, Surface reconstruction, Convolution, Neural networks, Estimation, Feature extraction, Vision applications and systems BibRef

Lee, D.[Donghoon], Hamsici, O.C.[Onur C.], Feng, S.[Steven], Sharma, P.[Prachee], Gernoth, T.[Thorsten],
DeepPRO: Deep Partial Point Cloud Registration of Objects,
ICCV21(5663-5672)
IEEE DOI 2203
Point cloud compression, Solid modeling, Shape, Databases, Transforms, Real-time systems, Stereo, Vision applications and systems BibRef

Agostinho, S.[Sérgio], Ošep, A.[Aljoša], del Bue, A.[Alessio], Leal-Taixé, L.[Laura],
(Just) A Spoonful of Refinements Helps the Registration Error Go Down,
ICCV21(6088-6097)
IEEE DOI 2203
Training, Point cloud compression, Linear systems, Estimation, Mathematical models, Stereo, 3D from multiview and other sensors, Optimization and learning methods BibRef

Deng, Z.[Zhi], Yao, Y.X.[Yu-Xin], Deng, B.[Bailin], Zhang, J.[Juyong],
A Robust Loss for Point Cloud Registration,
ICCV21(6118-6127)
IEEE DOI 2203
Measurement, Point cloud compression, Learning systems, Shape, Optimization, Stereo, 3D from multiview and other sensors, Motion and tracking BibRef

Liu, W.X.[Wei-Xiao], Wu, H.T.[Hong-Tao], Chirikjian, G.S.[Gregory S.],
LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration,
ICCV21(15273-15282)
IEEE DOI 2203
Point cloud compression, Geometry, Maximum likelihood estimation, Laser radar, Graphics processing units, Optimization methods, Vision applications and systems BibRef

Min, T.[Taewon], Song, C.[Chonghyuk], Kim, E.S.[Eun-Seok], Shim, I.[Inwook],
Distinctiveness oriented Positional Equilibrium for Point Cloud Registration,
ICCV21(5470-5478)
IEEE DOI 2203
Point cloud compression, Refining, Benchmark testing, Graph neural networks, Feeds, Task analysis, Stereo, Recognition and classification BibRef

Xu, H.[Hao], Liu, S.C.[Shuai-Cheng], Wang, G.[Guangfu], Liu, G.H.[Guang-Hui], Zeng, B.[Bing],
OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration,
ICCV21(3112-3121)
IEEE DOI 2203
Point cloud compression, Deep learning, Solid modeling, Shape, Computational modeling, 3D from multiview and other sensors BibRef

Horache, S.[Sofiane], Deschaud, J.E.[Jean-Emmanuel], Goulette, F.[François],
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning,
3DV21(1351-1361)
IEEE DOI 2201
Point cloud compression, Deep learning, Codes, Convolution, Transfer learning, Supervised learning, Deep Learning, Registration BibRef

Lang, I.[Itai], Ginzburg, D.[Dvir], Avidan, S.[Shai], Raviv, D.[Dan],
DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction,
3DV21(1442-1451)
IEEE DOI 2201
Point cloud compression, Codes, Shape, Animals, Computational modeling, Training data, 3D Point Clouds, Real Time BibRef

Saponaro, M., Capolupo, A., Caporusso, G., Tarantino, E.,
Influence of Co-alignment Procedures on the Co-registration Accuracy Of Multi-Epoch SFM Points Clouds,
ISPRS21(B2-2021: 231-238).
DOI Link 2201
BibRef

Zhan, K., Fritsch, D., Wagner, J.F.,
Photogrammetry and Computed Tomography Point Cloud Registration Using Virtual Control Points,
ISPRS21(B2-2021: 265-270).
DOI Link 2201
BibRef

Partovi, T., Dähne, M., Maboudi, M., Krueger, D., Gerke, M.,
Automatic Integration of Laser Scanning and Photogrammetric Point Clouds: From Acquisition to Co-registration,
ISPRS21(B1-2021: 85-92).
DOI Link 2201
BibRef

Tun, S.W.[Su Wai], Komuro, T.[Takashi], Nagahara, H.[Hajime],
3D Registration of Deformable Objects Using a Time-of-Flight Camera,
ISVC21(I:455-465).
Springer DOI 2112
BibRef

Efraim, A.[Amit], Francos, J.M.[Joseph M.],
Dual Transformation and Manifold Distances Voting for Outlier Rejection in Point Cloud Registration,
TAG-CV21(4187-4195)
IEEE DOI 2112
Manifolds, Conferences BibRef

Ren, Z.Z.[Zhong-Zheng], Misra, I.[Ishan], Schwing, A.G.[Alexander G.], Girdhar, R.[Rohit],
3D Spatial Recognition without Spatially Labeled 3D,
CVPR21(13199-13208)
IEEE DOI 2111
Training, Couplings, Semantics, Object detection, Pattern recognition BibRef

Deng, Y.[Yu], Yang, J.L.[Jiao-Long], Tong, X.[Xin],
Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence,
CVPR21(10281-10291)
IEEE DOI 2111
Deformable models, Solid modeling, Uncertainty, Shape, Shape measurement, Neural networks BibRef

Zeng, Y.M.[Yi-Ming], Qian, Y.[Yue], Zhu, Z.Y.[Zhi-Yu], Hou, J.H.[Jun-Hui], Yuan, H.[Hui], He, Y.[Ying],
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds,
CVPR21(6048-6057)
IEEE DOI 2111
Symmetric matrices, Sequences, Shape, Supervised learning, Redundancy, Transforms BibRef

Thalhammer, S.[Stefan], Patten, T.[Timothy], Vincze, M.[Markus],
COPE: End-to-end trainable Constant Runtime Object Pose Estimation,
WACV23(2859-2869)
IEEE DOI 2302
Runtime, Pose estimation, Prediction algorithms, Real-time systems, Task analysis, Standards, Algorithms: 3D computer vision, visual reasoning BibRef

Bauer, D.[Dominik], Patten, T.[Timothy], Vincze, M.[Markus],
ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning,
CVPR21(14581-14589)
IEEE DOI 2111
Solid modeling, Pose estimation, Reinforcement learning, Real-time systems, Trajectory BibRef

Ao, S.[Sheng], Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Markham, A.[Andrew], Guo, Y.L.[Yu-Lan],
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration,
CVPR21(11748-11757)
IEEE DOI 2111
Convolutional codes, Detectors, Feature extraction, Transformers BibRef

El Banani, M.[Mohamed], Gao, L.[Luya], Johnson, J.[Justin],
UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering,
CVPR21(7125-7135)
IEEE DOI 2111
Training, Simultaneous localization and mapping, Robot vision systems, Feature extraction, Sensors BibRef

Huang, S.Y.[Sheng-Yu], Gojcic, Z.[Zan], Usvyatsov, M.[Mikhail], Wieser, A.[Andreas], Schindler, K.[Konrad],
PREDATOR: Registration of 3D Point Clouds with Low Overlap,
CVPR21(4265-4274)
IEEE DOI 2111
Convolutional codes, Solid modeling, Image matching, Encoding BibRef

Bai, X.Y.[Xu-Yang], Luo, Z.X.[Zi-Xin], Zhou, L.[Lei], Chen, H.K.[Hong-Kai], Li, L.[Lei], Hu, Z.[Zeyu], Fu, H.B.[Hong-Bo], Tai, C.L.[Chiew-Lan],
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency,
CVPR21(15854-15864)
IEEE DOI 2111
Deep learning, Costs, Codes, Spatial coherence, Robustness BibRef

Zhao, Y.X.[Ya-Xin], Jiao, J.[Jichao], Li, N.[Ning], Deng, Z.L.[Zhong-Liang],
MANet: Multimodal Attention Network based Point-View Fusion for 3D Shape Recognition,
ICPR21(134-141)
IEEE DOI 2105
Deep learning, Image recognition, Shape, Fuses, Neural networks, Big Data, point-cloud, multi-view, multimodel attention network BibRef

Pan, X.[Xiang], Ji, X.Y.[Xiao-Yi], Cheng, S.[Sisi],
3D Point Cloud Registration Based on Cascaded Mutual Information Attention Network,
ICPR21(10644-10649)
IEEE DOI 2105
Correlation, Stacking, Pattern recognition, Reliability, Mutual information, Convergence BibRef

Zodage, T., Chakwate, R., Sarode, V., Srivatsan, R.A., Choset, H.,
Correspondence Matrices are Underrated,
3DV20(603-612)
IEEE DOI 2102
Registers, Training, Task analysis, Perturbation methods, Robustness, Matrix converters, registration, partial point cloud BibRef

Saleh, M., Dehghani, S., Busam, B., Navab, N.,
Graphite: Graph-Induced Feature Extraction for Point Cloud Registration,
3DV20(241-251)
IEEE DOI 2102
Feature extraction, Graphite, Pipelines, Measurement, Data mining, Graph Neural Networks BibRef

Kadam, P., Zhang, M., Liu, S., Kuo, C.C.J.,
Unsupervised Point Cloud Registration via Salient Points Analysis (SPA),
VCIP20(5-8)
IEEE DOI 2102
Deep learning, Training, Visual communication, Image processing, Registers, unsupervised machine learning BibRef

Kuo, C.C.J.[C.C. Jay],
Interpretable and Effective Learning for 3D Point Cloud Registration, Classification and Segmentation,
VCIP20(1-2)
IEEE DOI 2102
Task analysis, Visualization, Training, Three-dimensional printing, Multimedia computing BibRef

Krahn, M.[Maximilian], Bernard, F.[Florian], Golyanik, V.[Vladislav],
Convex Joint Graph Matching and Clustering via Semidefinite Relaxations,
3DV21(1216-1226)
IEEE DOI 2201
Couplings, Codes, Training data, Hilbert space, Cognition, Compounds, joint graph matching and clustering, semidefinite relaxation, rigid point set registration BibRef

Golyanik, V.[Vladislav], Shimada, S., Theobalt, C.,
Fast Simultaneous Gravitational Alignment of Multiple Point Sets,
3DV20(91-100)
IEEE DOI 2102
Acceleration, Complexity theory, Transforms, Solid modeling, Runtime, Probabilistic logic BibRef

Zhu, N., Yang, B., Jia, Y.,
Registration of MMS Lidar Points and Panoramic Image Sequence Using Relative Orientation Model,
ISPRS20(B1:291-298).
DOI Link 2012
BibRef

Ai, M., Liu, C., Shen, H., Cheng, F.,
Indoor Scene Registration Based on Key Points Sampling and Hierarchical Feature Learning,
ISPRS20(B2:177-182).
DOI Link 2012
BibRef

Li, J.H.[Jia-Hao], Zhang, C.H.[Chang-Hao], Xu, Z.[Ziyao], Zhou, H.N.[Hang-Ning], Zhang, C.[Chi],
Iterative Distance-aware Similarity Matrix Convolution with Mutual-supervised Point Elimination for Efficient Point Cloud Registration,
ECCV20(XXIV:378-394).
Springer DOI 2012
BibRef

Urbach, D.[Dahlia], Ben-Shabat, Y.Z.[Yi-Zhak], Lindenbaum, M.[Michael],
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance,
ECCV20(XI:545-560).
Springer DOI 2011
BibRef

Yang, L.[Lei], Liu, W.X.[Wen-Xi], Cui, Z.M.[Zhi-Ming], Chen, N.L.[Neng-Lun], Wang, W.P.[Wen-Ping],
Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes,
ECCV20(X:455-472).
Springer DOI 2011
BibRef

Yang, Z.T.[Ze-Tong], Sun, Y.[Yanan], Liu, S.[Shu], Qi, X.J.[Xiao-Juan], Jia, J.Y.[Jia-Ya],
CN: Channel Normalization for Point Cloud Recognition,
ECCV20(X:600-616).
Springer DOI 2011
BibRef

Sanghi, A.[Aditya],
Info3D: Representation Learning on 3D Objects Using Mutual Information Maximization and Contrastive Learning,
ECCV20(XXIX: 626-642).
Springer DOI 2010
BibRef

Mei, G.F.[Guo-Feng],
Point Cloud Registration with Self-supervised Feature Learning and Beam Search,
DICTA21(01-08)
IEEE DOI 2201
Point cloud compression, Representation learning, Training, Shape, Digital images, Neural networks, point cloud registration, beam search BibRef

Huang, X.S.[Xiao-Shui], Mei, G.F.[Guo-Feng], Zhang, J.[Jian],
Feature-Metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration Without Correspondences,
CVPR20(11363-11371)
IEEE DOI 2008
Feature extraction, Estimation, Task analysis, Robustness, Jacobian matrices, Neural networks BibRef

Gojcic, Z., Zhou, C., Wegner, J.D., Guibas, L.J.[Leonidas J.], Birdal, T.[Tolga],
Learning Multiview 3D Point Cloud Registration,
CVPR20(1756-1766)
IEEE DOI 2008
Synchronization, Closed-form solutions, Pipelines, Task analysis, Robustness, Estimation BibRef

Chen, J., Zhou, F., Liu, B., Bai, X., Zhang, Y., Zhao, T., Li, N., Zhou, Y.,
3D Rigid Registration of Patient Body Surface Point Clouds by Integer Linear Programming,
IVCNZ19(1-6)
IEEE DOI 2004
computerised tomography, feature extraction, graph theory, image matching, image registration, integer programming, feature correspondence BibRef

Zhao, J., Qi, X., Wen, C., Lei, N., Gu, X.,
Automatic and Robust Skull Registration Based on Discrete Uniformization,
ICCV19(431-440)
IEEE DOI 2004
combinatorial mathematics, image reconstruction, image registration, medical image processing, mesh generation, Face BibRef

Gojcic, Z.[Zan], Zhou, C.F.[Cai-Fa], Wegner, J.D.[Jan D.], Wieser, A.[Andreas],
The Perfect Match: 3D Point Cloud Matching With Smoothed Densities,
CVPR19(5540-5549).
IEEE DOI 2002
BibRef

Donne, S.[Simon], Geiger, A.[Andreas],
Learning Non-Volumetric Depth Fusion Using Successive Reprojections,
CVPR19(7626-7635).
IEEE DOI 2002
BibRef

Li, X.Q.[Xue-Qian], Pontes, J.K.[Jhony Kaesemodel], Lucey, S.[Simon],
PointNetLK Revisited,
CVPR21(12758-12767)
IEEE DOI 2111
Training, Jacobian matrices, Solid modeling, Robustness, Sensors, Pattern recognition BibRef

Aoki, Y.[Yasuhiro], Goforth, H.[Hunter], Srivatsan, R.A.[Rangaprasad Arun], Lucey, S.[Simon],
PointNetLK: Robust and Efficient Point Cloud Registration Using PointNet,
CVPR19(7156-7165).
IEEE DOI 2002
BibRef

Le, H.M.[Huu M.], Do, T.T.[Thanh-Toan], Hoang, T.[Tuan], Cheung, N.M.[Ngai-Man],
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration Without Correspondences,
CVPR19(124-133).
IEEE DOI 2002
BibRef

Deng, H.[Haowen], Birdal, T.[Tolga], Ilic, S.[Slobodan],
3D Local Features for Direct Pairwise Registration,
CVPR19(3239-3248).
IEEE DOI 2002
BibRef

Moradi, L., Saadatseresht, M.,
Simultaneous Registration and Integration of Two Sequential Velodyne Point Clouds Using Voxel-based Least Square Adjustment,
SMPR19(759-763).
DOI Link 1912
BibRef

Huang, R., Ye, Z., Boerner, R., Yao, W., Xu, Y., Stilla, U.,
Fast Pairwise Coarse Registration Between Point Clouds of Construction Sites Using 2d Projection Based Phase Correlation,
Laser19(1015-1020).
DOI Link 1912
BibRef

Groß, J.[Johannes], Ošep, A.[Aljoša], Leibe, B.[Bastian],
AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects,
3DV19(623-632)
IEEE DOI 1911
Laser radar, Target tracking, State estimation, Task analysis, Motion segmentation, Sensors, Robotics BibRef

Wong, X.I., Singla, P., Lee, T., Majji, M.,
Optimal Linear Attitude Estimator for Alignment of Point Clouds,
Odometry18(1577-15778)
IEEE DOI 1812
Symmetric matrices, Transmission line matrix methods, Matrix decomposition, Robot sensing systems BibRef

Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.,
PCN: Point Completion Network,
3DV18(728-737)
IEEE DOI 1812
learning (artificial intelligence), object detection, optical radar, radar computing, point cloud registration BibRef

Eckart, B., Kim, K., Jan, K.,
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data,
3DV18(747-755)
IEEE DOI 1812
expectation-maximisation algorithm, Gaussian processes, image registration, iterative methods, overlap estimation BibRef

Su, H.[Hang], Jampani, V.[Varun], Sun, D.Q.[De-Qing], Maji, S.[Subhransu], Kalogerakis, E.[Evangelos], Yang, M.H.[Ming-Hsuan], Kautz, J.[Jan],
SPLATNet: Sparse Lattice Networks for Point Cloud Processing,
CVPR18(2530-2539)
IEEE DOI 1812
Award, CVPR, HM. Lattices, Convolution, Shape, Standards BibRef

Pan, Y., Yang, B., Liang, F., Dong, Z.,
Iterative Global Similarity Points: A Robust Coarse-to-Fine Integration Solution for Pairwise 3D Point Cloud Registration,
3DV18(180-189)
IEEE DOI 1812
feature extraction, geometry, image matching, image registration, iterative methods, iterative global similarity points, energy optimization BibRef

Xu, D., Anguelov, D., Jain, A.,
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation,
CVPR18(244-253)
IEEE DOI 1812
Solid modeling, Robot sensing systems, Laser radar, Object detection BibRef

Sekkati, H., Boisvert, J., Godin, G., Borgeat, L.,
Real-Time Large-Scale Fusion of High Resolution 3D Scans with Details Preservation,
CRV18(63-70)
IEEE DOI 1812
Solid modeling, Cameras, Graphics processing units, Real-time systems, Shape, GPU BibRef

Liu, Y.L.[Yin-Long], Wang, C.[Chen], Song, Z.J.[Zhi-Jian], Wang, M.[Manning],
Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search,
ECCV18(XII: 460-474).
Springer DOI 1810
BibRef

Yew, Z.J.[Zi Jian], Lee, G.H.[Gim Hee],
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration,
ECCV18(XV: 630-646).
Springer DOI 1810
BibRef

Shen, T.W.[Tian-Wei], Luo, Z.X.[Zi-Xin], Zhou, L.[Lei], Zhang, R.[Runze], Zhu, S.[Siyu], Fang, T.[Tian], Quan, L.[Long],
Matchable Image Retrieval by Learning from Surface Reconstruction,
ACCV18(I:415-431).
Springer DOI 1906
Code and data:
WWW Link. Retrieve images with overlaps for 3D reconstructions. BibRef

Seib, V., Paulus, D.,
A Low-Dimensional Feature Transform for Keypoint Matching and Classification of Point Clouds without Normal Computation,
ICIP18(2949-2953)
IEEE DOI 1809
Histograms, Transforms, Shape, Computational modeling, Indexes, Pipelines, Descriptor Transform, Shape Classification BibRef

Peng, H.L.[Hong-Li], Shen, Z.[Zhen], Shang, X.Q.[Xiu-Qin], Liu, X.W.[Xi-Wei], Xiong, G.[Gang], Liu, T.Z.[Tao-Zhong], Nyberg, T.R.[Timo R.],
Foot Modeling Based on Machine Vision and Social Manufacturing Research,
PSIVTWS17(144-157).
Springer DOI 1806
BibRef

Gézero, L., Antunes, C.,
A Registration Method of Point Clouds Collected By Mobile Lidar Using Solely Standard Las Files Information,
Hannover17(121-128).
DOI Link 1805
BibRef

Karkalou, E., Stentoumis, C., Karras, G.,
Semi-global Matching with Self-adjusting Penalties,
3DARCH17(353-360).
DOI Link 1805
BibRef

Che Ku Abdullah, C.K.A.F., Baharuddin, N.Z.S., Ariff, M.F.M., Majid, Z., Lau, C.L., Yusoff, A.R., Idris, K.M., Aspuri, A.,
Integration of Point Clouds Dataset From Different Sensors,
3DARCH17(9-15).
DOI Link 1805
BibRef

Chiem, Q.T.[Quang Tri], Wilkinson, R.H.[Richardt H.], Lech, M.[Margaret], Cheng2, E.[Eva],
Investigating Keypoint Repeatability for 3D Correspondence Estimation in Cluttered Scenes,
DICTA17(1-7)
IEEE DOI 1804
feature extraction, image matching, object detection, object recognition, 3D correspondence estimation, BibRef

Arvanitis, G., Spathis-Papadiotis, A., Lalos, A.S., Moustakas, K., Fakotakis, N.,
Outliers Removal and Consolidation of DYNAMIC Point Cloud,
ICIP18(3888-3892)
IEEE DOI 1809
Laplace equations, Interpolation, Sparse matrices, Surface treatment, Surface emitting lasers, weighted Laplacian interpolation BibRef

Arvanitis, G., Lalos, A.S., Moustakas, K., Fakotakis, N.,
Weighted Regularized Laplacian Interpolation for Consolidation of Highly-Incomplete Time Varying Point Clouds,
3DTV-CON17(1-4)
IEEE DOI 1804
image reconstruction, interpolation, highly-incomplete time varying point clouds, weighted Laplacian interpolation BibRef

Zanuttigh, P., Minto, L.,
Deep learning for 3D shape classification from multiple depth maps,
ICIP17(3615-3619)
IEEE DOI 1803
Convolutional neural networks, Machine learning, Shape, Solid modeling, Task analysis, Depth Map BibRef

Shafiq, U., Taj, M., Ali, M.,
More for less: Insights into convolutional nets for 3D point cloud recognition,
ICIP17(1607-1611)
IEEE DOI 1803
Convolution, Shape, Solid modeling, Training, recognition BibRef

Klokov, R., Lempitsky, V.[Victor],
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models,
ICCV17(863-872)
IEEE DOI 1802
learning (artificial intelligence), shape recognition, solid modelling, trees (mathematics), 3D point cloud models, BibRef

Avidar, D., Malah, D., Barzohar, M.,
Local-to-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors,
ICCV17(891-899)
IEEE DOI 1802
discrete Fourier transforms, feature extraction, image registration, Airborne Laser Scanning, DFT domain, Urban areas BibRef

Briales, J., Gonzalez-Jimenez, J.,
Convex Global 3D Registration with Lagrangian Duality,
CVPR17(5612-5621)
IEEE DOI 1711
Optimization, Pipelines, Proposals, Simultaneous localization and mapping, BibRef

Golyanik, V., Reis, G., Taetz, B., Strieker, D.,
A framework for an accurate point cloud based registration of full 3D human body scans,
MVA17(67-72)
DOI Link 1708
Foot, Image reconstruction, Pipelines, Semantics, Skeleton, Topology BibRef

Matusiak, K., Skulimowski, P., Strumillo, P.,
Improving matching performance of the keypoints in images of 3D scenes by using depth information,
WSSIP17(1-5)
IEEE DOI 1707
Algorithm design and analysis, Detectors, Feature extraction, Image edge detection, Object recognition, depth map, feature matching, keypoints detection, object, recognition BibRef

Psarrou, A., Angelopoulou, A., Mentzelopoulos, M., García-Rodríguez, J.,
Performance evaluation of a statistical and a neural network model for nonrigid shape-based registration,
IPTA16(1-6)
IEEE DOI 1703
Hebbian learning BibRef

Casanova, A., Pujol-Miró, A., Ruiz-Hidalgo, J., Casas, J.R.,
Interactive registration method for 3D data fusion,
IC3D16(1-8)
IEEE DOI 1703
image registration BibRef

Yi, Z.L.[Zi-Li], Li, Y.[Yang], Gong, M.L.[Ming-Lun],
An Efficient Algorithm for Feature-Based 3D Point Cloud Correspondence Search,
ISVC16(I: 485-496).
Springer DOI 1701
BibRef

Ma, Y., Guo, Y., Zhao, J., Lu, M., Zhang, J., Wan, J.,
Fast and Accurate Registration of Structured Point Clouds with Small Overlaps,
LS3D16(643-651)
IEEE DOI 1612
BibRef

Kang, Z., Lindenbergh, R., Pu, S.,
Speeding Up Coarse Point Cloud Registration By Threshold-independent Baysac Match Selection,
ISPRS16(B5: 493-500).
DOI Link 1610
BibRef

Bueno, M., Martínez-Sánchez, J., González-Jorge, H., Lorenzo, H.,
Detection Of Geometric Keypoints And Its Application To Point Cloud Coarse Registration,
ISPRS16(B3: 187-194).
DOI Link 1610
BibRef

Attia, M., Slama, Y., Kamoun, M.A.,
On Performance Evaluation of Registration Algorithms for 3D Point Clouds,
CGiV16(45-50)
IEEE DOI 1608
computational geometry BibRef

Al-Nuaimi, A., Steinbach, E., Lopes, W.B., Lopes, C.G.,
6DOF point cloud alignment using geometric algebra-based adaptive filtering,
WACV16(1-9)
IEEE DOI 1606
Calculus BibRef

Qiu, R.Q.[Rong-Qi], Neumann, U.[Ulrich],
IPDC: Iterative part-based dense correspondence between point clouds,
WACV16(1-9)
IEEE DOI 1606
Colored noise BibRef

Ahmed, M.T.[Mirza Tahir], Mohamad, M.[Mustafa], Marshall, J.A.[Joshua A.], Greenspan, M.[Michael],
Registration of Noisy Point Clouds Using Virtual Interest Points,
CRV15(31-38)
IEEE DOI 1507
Feature extraction BibRef

Lachhani, K.[Kishan], Duan, J.F.[Ji-Fang], Baghsiahi, H.[Hadi], Willman, E.[Eero], Selviah, D.R.[David R.],
Correspondence rejection by trilateration for 3D point cloud registration,
MVA15(337-340)
IEEE DOI 1507
Estimation BibRef

Du, J.[Jia], Xiong, W.[Wei], Chen, W.Y.[Wen-Yu], Cheng, J.[Jierong], Wang, Y.[Yue], Gu, Y.[Ying], Chia, S.C.[Shue-Ching],
Multi-view Point Cloud Registration Using Affine Shape Distributions,
ACCV14(II: 147-161).
Springer DOI 1504
BibRef

Lu, M.[Min], Zhao, J.[Jian], Guo, Y.L.[Yu-Lan], Ou, J.P.[Jian-Ping], Li, J.,
A 3D pointcloud registration algorithm based on fast coherent point drift,
AIPR14(1-6)
IEEE DOI 1504
computational complexity BibRef

Papon, J.[Jeremie], Schoeler, M.[Markus], Worgotter, F.[Florentin],
Spatially Stratified Correspondence Sampling for Real-Time Point Cloud tracking,
WACV15(124-131)
IEEE DOI 1503
Accuracy
See also Voxel Cloud Connectivity Segmentation: Supervoxels for Point Clouds. BibRef

Matsopoulos, G.K.[George K.], Economopoulos, T.L., Karanasiou, I.S., Koutsoupidou, M., Ventouras, E.,
Alignment of three-dimensional point clouds using combined descriptors,
IPTA14(1-5)
IEEE DOI 1503
computational geometry BibRef

Transue, S.[Shane], Choi, M.H.[Min-Hyung],
Intuitive Alignment of Point-Clouds with Painting-Based Feature Correspondence,
ISVC14(II: 746-756).
Springer DOI 1501
BibRef

Deng, Y.[Yan], Rangarajan, A.[Anand], Eisenschenk, S.[Stephan], Vemuri, B.C.[Baba C.],
A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform,
CVPR14(3756-3761)
IEEE DOI 1409
Point clouds matching BibRef

Ridene, T., Goulette, F., Chendeb, S.,
Feature-Based Quality Evaluation of 3D Point Clouds: Study of the Performance of 3D Registration Algorithms,
GeoInfo13(59-64).
DOI Link 1402
BibRef

Altuntas, C.,
Integration of Point Clouds Originated from Laser Scaner and Photogrammetric Images for Visualization of Complex Details of Historical Buildings,
3D-Arch15(431-435).
DOI Link 1504
BibRef

Marques, M.[Manuel], Costeira, J.P.[Joao P.],
Guided search consensus: Large scale point cloud registration by convex optimization,
ICIP13(156-160)
IEEE DOI 1402
Cameras BibRef

Wang, C.P.[Chun-Po], Wilson, K., Snavely, N.,
Accurate Georegistration of Point Clouds Using Geographic Data,
3DV13(33-40)
IEEE DOI 1311
Internet BibRef

Palossi, D.[Daniele], Tombari, F.[Federico], Salti, S.[Samuele], Ruggiero, M.[Martino], di Stefano, L.[Luigi], Benini, L.[Luca],
GPU-SHOT: Parallel Optimization for Real-Time 3D Local Description,
ECVW13(584-591)
IEEE DOI 1309
3D descriptor; 3D feature; 3D object recognition; GPU optimization; SHOT BibRef

Weinmann, M., Dittrich, A., Hinz, S., Jutzi, B.,
Automatic Feature-Based Point Cloud Registration for a Moving Sensor Platform,
Hannover13(373-378).
DOI Link 1308
BibRef

Dyshkant, N.[Natalia],
Comparison of Point Clouds Acquired by 3D Scanner,
DGCI13(47-58).
Springer DOI 1304
BibRef

Varadarajan, K.M.[Karthik Mahesh], Vincze, M.[Markus],
Compressive Distance Classifier Correlation Filter,
ICIP13(3307-3311)
IEEE DOI 1402
BibRef
Earlier:
MRF Guided Anisotropic Depth Diffusion for Kinect Range Image Enhancement,
CDF12(II:223-235).
Springer DOI 1304
BibRef

Tsay, J.R., Lee, M.S.,
Sift for Dense Point Cloud Matching and Aero Triangulation,
ISPRS12(XXXIX-B3:69-74).
DOI Link 1209
BibRef

Hesabi, S.[Somayeh], Laurendeau, D.[Denis],
Aligning 3D Local Data of Leapfrog Locations along Elongated Structures,
CRV16(77-84)
IEEE DOI 1612
3D registration; alignment; cylinder; elongated structures; pipeline BibRef

Nguyen, V.T.[Van Tung], Tran, T.T.[Trung-Thien], Cao, V.T.[Van-Toan], Laurendeau, D.[Denis],
3D Point Cloud Registration Based on the Vector Field Representation,
ACPR13(491-495)
IEEE DOI 1408
computer graphics BibRef

Nguyen, V.T.[Van Tung], Laurendeau, D.[Denis],
A Global Registration Method Based on the Vector Field Representation,
CRV11(132-139).
IEEE DOI 1105
point cloud registration BibRef

Huhle, B., Schairer, T., Schilling, A., Strasser, W.,
6DoF Registration of 2D Laser Scans,
3DIMPVT11(148-155).
IEEE DOI 1109

See also Normalized Cross-Correlation using SOFT. BibRef

Herrmann, M.[Martin], Srinivasa, S.[Siddhartha],
Exploiting Passthrough Information for Multi-view Object Reconstruction with Sparse and Noisy Laser Data,
CMU-RI-TR-10-07, February, 2010.
WWW Link. 1102
laser scanner. Build model of an object. BibRef

Temerinac-Ott, M.[Maja], Keuper, M.[Margret], Burkhardt, H.[Hans],
Evaluation of a New Point Clouds Registration Method Based on Group Averaging Features,
ICPR10(2452-2455).
IEEE DOI 1008
BibRef

Detchev, I.[Ivan], Habib, A.[Ayman], Bang, K.I.[Ki In], Kersting, A.[Ana],
Automated multiple surface registration of irregular point clouds: A comparative analysis of two approaches,
CGC10(39).
PDF File. 1006
BibRef

Sehgal, A.[Anuj], Cernea, D.[Daniel], Makaveeva, M.[Milena],
Real-Time Scale Invariant 3D Range Point Cloud Registration,
ICIAR10(I: 220-229).
Springer DOI 1006
BibRef

Agugiaro, G., Kolbe, T.,
Definition of a transition surface with the purpose of integration between a laser scanner 3D model and a low resolution DTM,
3DARCH09(xx-yy).
PDF File. 0902
BibRef

Molkenstruck, S.[Sven], Winkelbach, S.[Simon], Wahl, F.M.[Friedrich M.],
3D Body Scanning in a Mirror Cabinet,
DAGM08(xx-yy).
Springer DOI 0806
BibRef
Earlier: A2, A1, A3:
Low-Cost Laser Range Scanner and Fast Surface Registration Approach,
DAGM06(718-728).
Springer DOI 0610
Award, GCPR. BibRef

Mahmoudi, M.[Mona], Sapiro, G.[Guillermo],
Three-dimensional point cloud recognition via distributions of geometric distances,
S3D08(1-8).
IEEE DOI 0806
BibRef

Biswas, S., Aggarwal, G., Chellappa, R.,
Invariant Geometric Representation of 3D Point Clouds for Registration and Matching,
ICIP06(1209-1212).
IEEE DOI 0610
BibRef

Makadia, A.[Ameesh], Patterson, A.I.[Alexander I.], Daniilidis, K.[Kostas],
Fully Automatic Registration of 3D Point Clouds,
CVPR06(I: 1297-1304).
IEEE DOI 0606
Correlation of EGI in Fourier domain. BibRef

Jagannathan, A., Miller, E.L.,
Unstructured Point Cloud Matching within Graph-Theoretic and Thermodynamic Frameworks,
CVPR05(II: 1008-1015).
IEEE DOI 0507
BibRef

Mure-Dubois, J.[James], Hugli, H.[Heinz],
Fusion of Time of Flight Camera Point Clouds,
M2SFA208(xx-yy). 0810
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
RGB-D Registeration, RGBD Registraion, Color and LiDAR .