12.3.4.1.2 Non-Rigid Point Cloud Registration, Deformable Point Cloud

Chapter Contents (Back)
Non-Rigid Registration. Deformable Registration. Registration, 3-D. Registration. LiDAR Registration. Point Clouds. Point Cloud Registration. 3-D. 2607

See also Non-Rigid Image Registration, Deformable Registration, Techniques.

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

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

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

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

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

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

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

Zhang, T.[Tian], Elnashef, B.[Bashar], Filin, S.[Sagi],
Spatio-temporal registration of plants non-rigid 3-D structure,
PandRS(205), 2023, pp. 263-283.
Elsevier DOI 2311
Spatio-temporal models, Point clouds, Non-rigid 3-D registration, Phenotyping, Precision agriculture BibRef

Ma, L.T.[Li-Tao], Bian, W.[Wei], Xue, X.P.[Xiao-Ping],
Point Clouds Matching Based on Discrete Optimal Transport,
IP(33), 2024, pp. 5650-5662.
IEEE DOI 2410
Point cloud compression, US Department of Transportation, Deformation, Noise, Image matching, Adaptation models, STEM, prior probability BibRef

Wang, L.[Ling], Chen, R.[Runfa], Sun, F.C.[Fu-Chun], Wang, X.Z.[Xin-Zhou], Sun, K.[Kai], Zhong, C.L.[Cheng-Liang], Fu, G.Y.[Guang-Yuan], Wang, Y.K.[Yi-Kai],
Equivariant Local Reference Frames with Optimization for Robust Non-Rigid Point Cloud Correspondence,
IP(34), 2025, pp. 1980-1994.
IEEE DOI Code:
WWW Link. 2504
Shape, Point cloud compression, Semantics, Vectors, Context modeling, Complexity theory, Training, Sun, Optimization, equivariant neural networks BibRef

Wang, J.Y.[Jin-Yang], Lu, X.Q.[Xue-Quan], Bennamoun, M.[Mohammed], Sheng, B.[Bin],
Non-Rigid Point Cloud Registration via Anisotropic Hybrid Field Harmonization,
PAMI(47), No. 9, September 2025, pp. 7898-7915.
IEEE DOI 2508
Point cloud compression, Deformation, Anisotropic, Manifolds, Shape, Optimization, Geometry, Australia, Vectors, symplectic manifolds BibRef

Chen, H.Z.[Hao-Zhe], Li, R.[Rui], Guo, J.M.[Jia-Ming], He, Y.[Ya'nan], Wang, Z.B.[Zheng-Bao], Han, X.F.[Xian-Feng], Sun, K.[Kun], Yang, J.Q.[Jia-Qi],
Robust Context Modeling for Unsupervised Non-Rigid Point Cloud Correspondence,
CirSysVideo(36), No. 6, June 2026, pp. 7526-7539.
IEEE DOI 2606
Point cloud compression, Context modeling, Deformation, Shape, Accuracy, Feature extraction, Pipelines, Benchmark testing, robust interplay BibRef

Ma, X.K.[Xin-Ke], Zeng, Q.J.[Qing-Jie], Hu, Y.[Yang], Lu, M.K.[Meng-Kang], Zhou, J.[Jie], Xia, Y.[Yong],
A variational Bayesian algorithm for probabilistic affine and non-rigid point cloud registration,
PR(179), 2026, pp. 113645.
Elsevier DOI Code:
WWW Link. 2606
Point cloud registration, Non-rigid registration, Variational inference, Affine transformation, Probabilistic model BibRef


Yang, Y.F.[Yi-Fei], Cao, S.[Sifan], Shao, L.[Long], Fan, J.F.[Jing-Fan], Yang, J.[Jian],
Iterative Similarity Perturbation Point Cloud Registration Based on Deformation-Resistant Region Detection,
ICIVC25(332-337)
IEEE DOI 2512
Point cloud compression, Training, Accuracy, Deformation, Navigation, Perturbation methods, Surgery, Robustness, Iterative methods, Surgical Navigation BibRef

Zeng, H.J.[Hua-Jian], Gao, M.L.[Mao-Lin], Cremers, D.[Daniel],
CoE: Deep Coupled Embedding for Non-Rigid Point Cloud Correspondences,
3DV25(286-295)
IEEE DOI 2512
Point cloud compression, Geometry, Shape, Deformation, Noise, Nearest neighbor methods, Benchmark testing, Robustness, Sensors, 3d representation BibRef

Chen, Z.Q.[Zhang-Quan], Jiang, P.[Puhua], Huang, R.[Ruqi],
DV-Matcher: Deformation-based Non-Rigid Point Cloud Matching Guided by Pre-trained Visual Features,
CVPR25(27264-27274)
IEEE DOI Code:
WWW Link. 2508
Point cloud compression, Representation learning, Deformable models, Visualization, Shape, Semantics, Manuals, pre-trained vision models 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

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

Chen, Y.F.[Yi-Fan], Pan, Z.[Zhiyu], Zhong, Z.C.[Zhi-Cheng], Guo, W.X.[Wen-Xuan], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie],
HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud,
3DV24(954-964)
IEEE DOI Code:
WWW Link. 2408
Point cloud compression, Annotations, Noise, Optimization methods, Estimation, Feature extraction BibRef

Heinrich, M.P.[Mattias P.], Bigalke, A.[Alexander], Großbröhmer, C.[Christoph], Hansen, L.[Lasse],
Chasing clouds: Differentiable volumetric rasterisation of point clouds as a highly efficient and accurate loss for large-scale deformable 3D registration,
ICCV23(7992-8002)
IEEE DOI Code:
WWW Link. 2401
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

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

Gay-Bellile, V., Perriollat, M., Bartoli, A.E., Sayd, P.,
Image Registration by Combining Thin-Plate Splines with a 3D Morphable Model,
ICIP06(1069-1072).
IEEE DOI 0610
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Outlier Detection, Analysis, Point Cloud Registration .


Last update:Jul 18, 2026 at 15:29:28