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https://github.com/XuyangBai/awesome-point-cloud-registration

A curated list of point cloud registration.
https://github.com/XuyangBai/awesome-point-cloud-registration
List: awesome-point-cloud-registration
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A curated list of point cloud registration.
Host: GitHub
URL: https://github.com/XuyangBai/awesome-point-cloud-registration
Owner: XuyangBai
Created: 2021-01-04T14:13:10.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2024-01-09T06:20:45.000Z (10 months ago)
Last Synced: 2024-04-18T14:38:40.125Z (7 months ago)
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Open Issues: 3
Metadata Files:
- Readme: README.md
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ultimate-awesome - awesome-point-cloud-registration - A curated list of point cloud registration. (Other Lists / PowerShell Lists)
README

        # awesome-point-cloud-registration

A curated list of resources on point cloud registration inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision). Work-in-progress. All contributions are welcome and appreciated.

This list focuses on the rigid registration between point clouds. 

## Table of Contents

- [Coarse Registration (Global Registration)](#coarse-registration)

    - [Feature Matching Based](#feature-matching-based)

        - [Keypoint Detection](#keypoint-detection)

        - [Feature Description](#feature-description)

        - [Outlier Rejection](#outlier-rejection)

        - [Graph Algorithms](#graph-algorithms)

    - [End-to-End](#end-to-end)

    - [Randomized](#randomized)

    - [Probablistic](#probabilistic)

- [Fine Registration (Local Registration)](#fine-registration)

    - [Learning-based](#learning-based)

    - [Traditional](#traditional)

- [Datasets](#datasets)

- [Tools](#tools)

---

## Coarse Registration

The coarse registration methods (or global registration) aligns two point clouds without an initial guess. We broadly classified these methods into feature matching based, end-to-end, randomized and probabilistic. Most of the learning based methods are focusing on some specific step in the feature matching based algorithms.

### Feature Matching Based

The feature-matching based registration algorithms generally follow a two-stage workflow: determining correspondence and estimate the transformation. The correspondence establishing stage usually follow the four-step pipeline: keypoint detection, feature description, descriptor matching and outlier rejection. The nearest neighbor matching is the de-facto matching strategy, but could be replaced by learnable matching stategies. We also include some papers which adopt the graph algorithms for the matching and outlier rejection problem.

#### Keypoint Detection

- HKS: A Concise and Provably Informative Multi‐Scale Signature Based on Heat Diffusion. CGF'2009 [[paper]](http://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Sun09.pdf)

- Harris3D: a robust extension of the harris operator for interest point detection on 3D meshes. VC'2011 [[paper]](http://www.ivan-sipiran.com/papers/SB11b.pdf)

- Intrinsic shape signatures: A shape descriptor for 3D object recognition. ICCV'2009 [[paper]](https://www.computer.org/csdl/proceedings/iccvw/2009/4442/00/05457637.pdf)

- Learning a Descriptor-Specific 3D Keypoint Detector. ICCV'2015 [[paper]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Salti_Learning_a_Descriptor-Specific_ICCV_2015_paper.pdf)

- 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. ECCV'2018 [[paper]](https://arxiv.org/pdf/1807.09413.pdf) [[code]](https://github.com/yewzijian/3DFeatNet)

- USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. ICCV'2019 [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.pdf) [[code]](https://github.com/lijx10/USIP)

- D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. CVPR'2020 [[paper]](https://arxiv.org/abs/2003.03164) [[code]](https://github.com/XuyangBai/D3Feat)

- PointCloud Saliency Maps. ICCV'2019 [[paper]](http://arxiv.org/pdf/1812.01687) [[code]](https://github.com/tianzheng4/PointCloud-Saliency-Maps)

- SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. AAAI'2020 [[paper]](https://arxiv.org/pdf/2003.14014.pdf)

- SKD: Unsupervised Keypoint Detecting for Point Clouds using Embedded Saliency Estimation. arxiv'2019 [[paper]](https://arxiv.org/pdf/1912.04943.pdf)

- Fuzzy Logic and Histogram of Normal Orientation-based 3D Keypoint Detection For Point Clouds. PRL'2020 [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S016786552030180X)

- MaskNet: A Fully-Convolutional Network to Estimate Inlier Points. 3DV'2020 [[paper]](https://arxiv.org/abs/2010.09185) [[code]](https://github.com/vinits5/masknet)

- PREDATOR: Registration of 3D Point Clouds with Low Overlap. arxiv'2020 [[paper]](https://arxiv.org/pdf/2011.13005.pdf) [[code]](https://github.com/ShengyuH/OverlapPredator)

- SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data. ICCV'2023 [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Zohaib_SC3K_Self-supervised_and_Coherent_3D_Keypoints_Estimation_from_Rotated_Noisy_ICCV_2023_paper.pdf) [[code]](https://github.com/IIT-PAVIS/SC3K)

Survey:

- Performance Evaluation of 3D Keypoint Detectors. IJCV'2013 [[paper]](https://doi.org/10.1007/s11263-012-0545-4)

#### Feature Description

- Spin Image: Using spin images for efficient object recognition in cluttered 3D scenes. TPAMI'1999 [[paper]](https://pdfs.semanticscholar.org/30c3/e410f689516983efcd780b9bea02531c387d.pdf?_ga=2.267321353.662069860.1609508014-1451995720.1602238989)

- USC: Unique shape context for 3D data description. 3DOR'2010 [[paper]](http://www.vision.deis.unibo.it/fede/papers/3dor10.pdf)

- 3DShapeContext: Recognizing Objects in Range Data Using Regional Point Descriptors. ECCV'2004 [[paper]](http://www.ri.cmu.edu/pub_files/pub4/frome_andrea_2004_1/frome_andrea_2004_1.pdf)

- SHOT: Unique Signatures of Histograms for Local Surface Description. ECCV'2010 [[paper]](http://www.researchgate.net/profile/Samuele_Salti/publication/262111100_SHOT_Unique_Signatures_of_Histograms_for_Surface_and_Texture_Description/links/541066b00cf2df04e75d5939.pdf)

- FPFH: Fast Point Feature Histograms (FPFH) for 3D registration. ICRA'2009 [[paper]](http://www.cvl.iis.u-tokyo.ac.jp/class2016/2016w/papers/6.3DdataProcessing/Rusu_FPFH_ICRA2009.pdf)

- RoPS: 3D Free Form Object Recognition using Rotational Projection Statistics. WACV'2013 [[paper]](http://www.researchgate.net/profile/Ferdous_Sohel/publication/236645183_3D_free_form_object_recognition_using_rotational_projection_statistics/links/0deec518a1038a2980000000.pdf)

- CGF: Learning Compact Geometric Features. ICCV'2017 [[paper]](http://arxiv.org/pdf/1709.05056)

- 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions. CVPR'2017 [[paper]](http://arxiv.org/pdf/1603.08182) [[code]](https://github.com/andyzeng/3dmatch-toolbox)

- End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching. CVPR'2018 [[paper]](http://arxiv.org/pdf/1802.07869)

- PPFNet: Global Context Aware Local Features for Robust 3D Point Matching. CVPR'2018 [[paper]](http://arxiv.org/pdf/1802.02669)

- 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. ECCV'2018 [[paper]](https://arxiv.org/pdf/1807.09413.pdf) [[code]](https://github.com/yewzijian/3DFeatNet)

- MVDesc: Learning and Matching Multi-View Descriptors for Registration of Point Clouds. ECCV'2018 [[paper]](https://arxiv.org/pdf/1807.05653.pdf) [[code]](https://github.com/zlthinker/RMBP)

- FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. CVPR'2018 [[paper]](http://arxiv.org/pdf/1712.07262) [[code]](https://github.com/XuyangBai/FoldingNet)

- PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. ECCV'2018 [[paper]](https://arxiv.org/abs/1808.10322) [[code]](https://github.com/XuyangBai/PPF-FoldNet)

- 3D Local Features for Direct Pairwise Registration. CVPR'2019 [[paper]](https://arxiv.org/abs/1904.04281)

- 3D Point-Capsule Networks. CVPR'2019 [[paper]](https://arxiv.org/abs/1812.10775) [[code]](https://github.com/yongheng1991/3D-point-capsule-networks)

- The Perfect Match: 3D Point Cloud Matching with Smoothed Densities. CVPR'2019 [[paper]](https://arxiv.org/abs/1811.06879) [[code]](https://github.com/zgojcic/3DSmoothNet)

- FCGF: Fully Convolutional Geometric Features. ICCV'2019 [[paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Choy_Fully_Convolutional_Geometric_Features_ICCV_2019_paper.pdf) [[code]](https://github.com/chrischoy/FCGF)

- Learning an Effective Equivariant 3D Descriptor Without Supervision. ICCV'2019 [[paper]](https://arxiv.org/abs/1909.06887)

- D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. CVPR'2020 [[paper]](https://arxiv.org/abs/2003.03164) [[code]](https://github.com/XuyangBai/D3Feat)

- End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds. CVPR'2020 [[paper]](https://arxiv.org/abs/2003.05855) [[code]](https://github.com/craigleili/3DLocalMultiViewDesc)

- LRF-Net- Learning Local Reference Frames for 3D Local Shape Description and Matching. arxiv'2020 [[paper]](https://arxiv.org/abs/2001.07832)

- DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. ECCV'2020 [[paper]](https://arxiv.org/pdf/2007.09217.pdf) [[code]](https://github.com/JuanDuGit/DH3D)

- Distinctive 3D local deep descriptors. arxiv'2020 [[paper]](https://arxiv.org/abs/2009.00258) [[code]](https://github.com/fabiopoiesi/dip)

- SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration. CVPR'2021 [[paper]](https://arxiv.org/abs/2011.12149) [[code]](https://github.com/QingyongHu/SpinNet)

- PREDATOR: Registration of 3D Point Clouds with Low Overlap. CVPR'2021 [[paper]](https://arxiv.org/pdf/2011.13005.pdf) [[code]](https://github.com/ShengyuH/OverlapPredator)

- Self-supervised Geometric Perception. CVPR'2021 [[paper]](https://arxiv.org/abs/2103.03114) [[code]](https://github.com/theNded/SGP)

- 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning. arxiv'2021 [[paper]](https://arxiv.org/abs/2103.14533) [[code]](https://github.com/humanpose1/MS-SVConv)

- Generalisable and Distinctive (GeDi) 3D local deep descriptors for point cloud registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2105.10382.pdf) [[code]](https://github.com/fabiopoiesi/gedi)

- Neighborhood Normalization for Robust Geometric Feature Learning. CVPR'2021 [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.pdf) [[code]](https://github.com/lppllppl920/NeighborhoodNormalization-Pytorch)

- UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering. CVPR'2021 [[paper]](https://arxiv.org/abs/2102.11870) [[code]](https://github.com/mbanani/unsupervisedRR)

- Bootstrap Your Own Correspondences. ICCV'2021 [[paper]](https://arxiv.org/abs/2106.00677) [[code]](https://github.com/mbanani/byoc)

- WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration. TVCG'2022 [[paper]](https://arxiv.org/abs/2108.02740) [[code]](https://github.com/craigleili/WSDesc)

- You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors. ICCV'2021 [[paper]](https://arxiv.org/abs/2109.00182) [[code]](https://github.com/HpWang-whu/YOHO)

- P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching. ICCV'2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_P2-Net_Joint_Description_and_Detection_of_Local_Features_for_Pixel_ICCV_2021_paper.pdf)

- Distinctiveness oriented Positional Equilibrium for Point Cloud Registration. ICCV'2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Min_Distinctiveness_Oriented_Positional_Equilibrium_for_Point_Cloud_Registration_ICCV_2021_paper.pdf)

- CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration. NeurIPS'2021 [[paper]](https://arxiv.org/pdf/2110.14076.pdf) [[code]](https://github.com/haoyu94/Coarse-to-fine-correspondences)

- IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2111.09624.pdf)

- Lepard: Learning partial point cloud matching in rigid and deformable scenes. CVPR'2022 [[paper]](https://arxiv.org/abs/2111.12591) [[code]](https://github.com/rabbityl/lepard)

- Fast and Robust Registration of Partially Overlapping Point Clouds. RA-L'2021 [[paper]](https://arxiv.org/pdf/2112.09922.pdf) [[code]](https://github.com/eduardohenriquearnold/fastreg)

- Geometric Transformer for Fast and Robust Point Cloud Registration. CVPR'2022 [[paper]](https://arxiv.org/abs/2202.06688) [[code]](https://github.com/qinzheng93/GeoTransformer)

- ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs. SIGGRAPH'2022 [[paper]](https://arxiv.org/pdf/2207.00826.pdf)

- Learning to Register Unbalanced Point Pairs. arxiv'2022 [[paper]](https://arxiv.org/abs/2207.04221)

 

Survey:

- A Comprehensive Performance Evaluation of 3D Local Feature Descriptors. IJCV'2015 [[paper]](https://link.springer.com/article/10.1007/s11263-015-0824-y)

- Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching. ICIP'2019 [[paper]](https://arxiv.org/abs/1907.00233)

#### Outlier Rejection

> We also include the algorithms designed for finding matching between keypoints given descriptors (which replaces nearest-neighbor-searching) in this section.

- RANSAC: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. 1981 [[paper]](http://www.cs.ait.ac.th/~mdailey/cvreadings/Fischler-RANSAC.pdf)

- Locally Optimized RANSAC. 2003 [[paper]](ftp://cmp.felk.cvut.cz/pub/cmp/articles/matas/chum-dagm03.pdf)

- Graph-cut RANSAC. CVPR'2018 [[paper]](https://arxiv.org/abs/1706.00984) [[code]](https://github.com/danini/graph-cut-ransac)

- MAGSAC: Marginalizing Sample Consensus. CVPR'2019 [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.pdf) [[code]](https://github.com/danini/magsac)

- VFC: A Robust Method for Vector Field Learning with Application To Mismatch Removing. CVPR'2011 [[paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.721.5913&rep=rep1&type=pdf)

- In Search of Inliers: 3D Correspondence by Local and Global Voting. CVPR'2014 [[paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Buch_In_Search_of_2014_CVPR_paper.pdf)

- FGR: Fast Global Registration. ECCV'2016 [[paper]](https://vladlen.info/publications/fast-global-registration/) [[code]](https://github.com/intel-isl/FastGlobalRegistration)

- Ranking 3D Feature Correspondences Via Consistency Voting. PRL'2019 [[paper]](https://doi.org/10.1016/j.patrec.2018.11.018)

- An Accurate and Efficient Voting Scheme for a Maximally All-Inlier 3D Correspondence Set. TPAMI'2020 [[paper]](https://ieeexplore.ieee.org/ielx7/34/4359286/08955806.pdf) 

- GORE: Guaranteed Outlier Removal for Point Cloud Registration with Correspondences. TPAMI'2018 [[paper]](https://arxiv.org/abs/1711.10209) [[code]](https://cs.adelaide.edu.au/~aparra/project/gore/)

- A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates. RSS'2019 [[paper]](https://arxiv.org/abs/1903.08588)

- Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection. ICRA'2020 [[paper]](https://arxiv.org/abs/1909.08605)

- TEASER: Fast and Certifiable Point Cloud Registration. T-RO'2020 [[paper]](https://arxiv.org/abs/2001.07715) [[code]](https://github.com/MIT-SPARK/TEASER-plusplus)

- One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers. NeurIPS'2020 [[paper]](https://arxiv.org/abs/2006.06769)

- SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences. CVPR'2019 [[paper]](https://arxiv.org/abs/1904.03483) [[code]](https://github.com/intellhave/SDRSAC)

- Robust Low-Overlap 3D Point Cloud Registration for Outlier Rejection. ICRA'2019 [[paper]](https://arpg.colorado.edu/papers/hmrf_icp.pdf)

- ICOS: Efficient and Highly Robust Rotation Search and Point Cloud Registration with Correspondences. arxiv'2021 [[paper]](https://arxiv.org/pdf/2104.14763.pdf)

- Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration. arxiv'2022 [[paper]](https://arxiv.org/pdf/2202.10579.pdf)

- A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments. ICRA'2022 [[paper]](https://arxiv.org/pdf/2203.06612.pdf) [[code]](https://github.com/url-kaist/quatro)

- SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. CVPR'2022 [[paper]](https://arxiv.org/abs/2203.14453) [[code]](https://github.com/ZhiChen902/SC2-PCR)

Learning based (including 2D outlier rejection methods)

- Learning to Find Good Correspondences. CVPR'2018 [[paper]](https://arxiv.org/abs/1711.05971) [[code]](https://github.com/vcg-uvic/learned-correspondence-release)

- NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences. CVPR'2019 [[paper]](https://arxiv.org/abs/1904.00320) [[code]](https://github.com/sailor-z/NM-Net)

- OANet: Learning Two-View Correspondences and Geometry using Order-Aware Network. ICCV'2019 [[paper]](https://arxiv.org/abs/1908.04964) [[code]](https://github.com/zjhthu/OANet)

- ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning. CVPR'2020 [[paper]](https://arxiv.org/abs/1907.02545) [[code]](https://github.com/vcg-uvic/acne)

- SuperGlue: Learning Feature Matching with Graph Neural Networks. CVPR'2020 [[paper]](https://arxiv.org/abs/1911.11763) [[code]](https://github.com/magicleap/SuperGluePretrainedNetwork)

- 3DRegNet: A Deep Neural Network for 3D Point Registration. CVPR'2020 [[paper]](https://arxiv.org/abs/1904.01701) [[code]](https://github.com/goncalo120/3DRegNet)

- Deep Global Registration. CVPR'2020 [[paper]](https://arxiv.org/abs/2004.11540) [[code]](https://github.com/chrischoy/DeepGlobalRegistration)

- 3D Correspondence Grouping with Compatibility Features. arxiv'2020 [[paper]](https://arxiv.org/pdf/2007.10570.pdf)

- PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency. CVPR'2021 [[paper]](https://arxiv.org/abs/2103.05465) [[code]](https://github.com/XuyangBai/PointDSC)

- StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks. CVPR'2021 [[paper]](https://arxiv.org/abs/2002.03983)

- HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration. ICCV'2021 [[paper]](https://arxiv.org/pdf/2107.11992.pdf) [[code]](https://github.com/ispc-lab/HRegNet)

- Keypoint Matching for Point Cloud Registration using Multiplex Dynamic Graph Attention Networks. RA-L'2021 [[paper]](https://ieeexplore.ieee.org/abstract/document/9484762)

- Deep Hough Voting for Robust Global Registration. ICCV'2021 [[paper]](http://jaesik.info/publications/data/21_iccv2.pdf)

- End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2110.15250.pdf)

- TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios. arxiv'2021 [[paper]](https://arxiv.org/abs/2111.00657)

- COTReg: Coupled Optimal Transport based Point Cloud Registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2112.14381.pdf)

- DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration. AAAI'2022 [[paper]](https://arxiv.org/pdf/2112.14059.pdf) [[code]](https://github.com/ZhiChen902/DetarNet)

- Multi-instance Point Cloud Registration by Efficient Correspondence Clustering. CVPR'2022 [[paper]](https://arxiv.org/pdf/2111.14582.pdf) [[code]](https://github.com/SJTU-ViSYS/multi-instant-reg)

Survey

- A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching. TPAMI'2019 [[paper]](http://arxiv.org/pdf/1907.02890)

#### Graph Algorithms

- A Graduated Assignment Algorithm for Graph Matching. TPAMI'1996 [[paper]](https://pdfs.semanticscholar.org/9899/003369af99d02f699cbcbf48b79019666158.pdf?_ga=2.31476793.662069860.1609508014-1451995720.1602238989)

- A Spectral Technique for Correspondence Problems Using Pairwise Constraints. ICCV'2005 [[paper]](https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/leordeanu-iccv-05.pdf) [[code]](https://sites.google.com/site/graphmatchingmethods/Code_including_Spectral_Matching.zip?attredirects=0)

- Balanced Graph Matching. NIPS'2006 [[paper]](http://papers.nips.cc/paper/2960-balanced-graph-matching) [[code]](https://github.com/afiliot/Balanced-Graph-Matching)

- Feature Correspondence via Graph Matching: Models and Global Optimization. ECCV'2008 [[paper]](https://www.microsoft.com/en-us/research/wp-content/uploads/2008/10/eccv08-MatchingMRF.pdf)

- An Integer Projected Fixed Point Method for Graph Matching and MAP Inference. NIPS'2009 [[paper]](https://www.cs.cmu.edu/~rahuls/pub/nips2009-rahuls.pdf) [[code]](https://sites.google.com/site/graphmatchingmethods/Code_including_IPFP.zip?attredirects=0)

- Optimal Correspondences From Pairwise Constraints. ICCV'2009 [[paper]](https://lup.lub.lu.se/search/ws/files/4311163/1454018.pdf)

- Reweighted Random Walks for Graph Matching. ECCV'2010 [[paper]](https://cv.snu.ac.kr/research/~RRWM/)

- Maximal Cliques Based Rigid Body Motion Segmentation with a RGB-D Camera. ACCV'2012 [[paper]](https://doi.org/10.1007/978-3-642-37444-9_10)

- A Probabilistic Approach to Spectral Graph Matching. TPAMI'2013 [[paper]](http://yosi-keller.narod.ru/publications/pdf/probabilistic-matching_rev3_two_columns.pdf)

- A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints. arxiv'2019 [[paper]](https://arxiv.org/pdf/1902.01534.pdf)

- ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants. arxiv'2020 [[paper]](https://arxiv.org/abs/2011.03659)

- CLIPPER A Graph-Theoretic Framework for Robust Data Association. arxiv'2020 [[paper]](https://arxiv.org/pdf/2011.10202.pdf)

- PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency CVPR'2021 [[paper]](https://arxiv.org/abs/2103.05465) [[code]](https://github.com/XuyangBai/PointDSC)

- Pairwise Point Cloud Registration Using Graph Matching and Rotation-invariant Features. arxiv'2021 [[paper]](https://arxiv.org/pdf/2105.02151.pdf)

- Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap. IROS'2023 [[paper]](https://arxiv.org/abs/2307.12116) [[code]](https://github.com/HKUST-Aerial-Robotics/Pagor)

- G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model. arxiv'2023 [[paper]](https://arxiv.org/abs/2308.11573) [[code]](https://github.com/HKUST-Aerial-Robotics/G3Reg)

### End-to-End

Some papers perform end-to-end registration by directly predicting a rigid transformation aligning two point clouds without explicitly following the `detection -- description -- outlier filtering` pipepline. While they works well on object-centric datasets, the performance on real-world scene registration is not satisfactory.

- PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. CVPR'2019 [[paper]](https://arxiv.org/abs/1903.05711) [[code]](https://github.com/hmgoforth/PointNetLK)

- Deep Closest Point: Learning Representations for Point Cloud Registration. ICCV'2019 [[paper]](https://arxiv.org/abs/1905.03304) [[code]](https://github.com/WangYueFt/dcp)

- PRNet: Self-Supervised Learning for Partial-to-Partial Registration. NeurIPS'2019 [[paper]](https://arxiv.org/abs/1910.12240) [[code]](https://github.com/WangYueFt/prnet)

- AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects. 3DV'2019 [[paper]](https://arxiv.org/abs/1910.04668) [[code]](https://github.com/grossjohannes/AlignNet-3D)

- RPM-Net: Robust Point Matching using Learned Features. CVPR'2020 [[paper]](https://arxiv.org/abs/2003.13479) [[code]](https://github.com/yewzijian/RPMNet)

- Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. CVPR'2020 [[code]](https://arxiv.org/abs/2005.01014) [[code]](https://github.com/XiaoshuiHuang/fmr)

- Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. ECCV'2020 [[paper]](http://arxiv.org/abs/1910.10328) [[code]](https://github.com/jiahaowork/idam)

- Self-supervised Point Set Local Descriptors for Point Cloud Registration. arxiv'2020 [[paper]](https://arxiv.org/pdf/2003.05199.pdf)

- Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration. arxiv'2020 [[paper]](https://arxiv.org/pdf/2006.04523.pdf)

- Robust Point Cloud Registration Framework Based on Deep Graph Matching. CVPR'2021 [[paper]](https://arxiv.org/abs/2103.04256) [[code]](https://github.com/fukexue/RGM)

- RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2^D-Tree Representation. CVPR'2021 [[paper]](https://arxiv.org/pdf/2104.05328.pdf)

- Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2105.02714.pdf)

- OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration. arxiv'2021 [[paper]](https://arxiv.org/pdf/2103.00937.pdf)

- FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration. AAAI'2022 [[paper]](https://arxiv.org/pdf/2106.03479.pdf) [[code]](https://github.com/hxwork/FINet_Pytorch)

- PointNetLK Revisited. CVPR'2021 [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_PointNetLK_Revisited_CVPR_2021_paper.pdf)

- Point Cloud Registration using Representative Overlapping Points. arxiv'2021 [[paper]](https://arxiv.org/pdf/2107.02583.pdf) [[code]](https://github.com/zhulf0804/ROPNet)

- Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations. arxiv'2021 [[paper]](https://arxiv.org/pdf/2107.10296.pdf)

- Geometry Guided Network for Point Cloud Registration. RA-L'2021 [[paper]](https://ieeexplore.ieee.org/abstract/document/9484764)

- (Just) A Spoonful of Refinements Helps the Registration Error Go Down. ICCV'2021 [[paper]](https://arxiv.org/pdf/2108.03257.pdf) [[code]](https://github.com/SergioRAgostinho/just-a-spoonful)

- A Robust Loss for Point Cloud Registration. ICCV'2021 [[paper]](https://arxiv.org/pdf/2108.11682.pdf)

- Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration. ICCV'2021 [[paper]](https://arxiv.org/pdf/2109.06619.pdf) [[code]](https://github.com/Jiang-HB/CEMNet)

- DeepBBS: Deep Best Buddies for Point Cloud Registration. 3DV'2021 [[paper]](https://arxiv.org/pdf/2110.03016.pdf) [[code]](https://github.com/itanhe/DeepBBS)

- PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds. ICCV'2021 [[paper]](https://arxiv.org/pdf/2110.01269.pdf) [[code]](https://github.com/valeoai/PCAM)

- Feature Interactive Representation for Point Cloud Registration. ICCV'2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wu_Feature_Interactive_Representation_for_Point_Cloud_Registration_ICCV_2021_paper.pdf)

- DeepPRO: Deep Partial Point Cloud Registration of Objects. ICCV'2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Lee_DeepPRO_Deep_Partial_Point_Cloud_Registration_of_Objects_ICCV_2021_paper.pdf)

- What Stops Learning-based 3D Registration from Working in the Real World? arxiv'2021 [[paper]](https://arxiv.org/abs/2111.10399)

- Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv'2021 [[paper]](https://arxiv.org/pdf/2112.09385.pdf) [[code]](https://github.com/CGuangyan-BIT/DIT)

- Reliable Inlier Evaluation for Unsupervised Point Cloud Registration. AAAI'2022 [[paper]](https://arxiv.org/pdf/2202.11292.pdf)

- VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration. arxiv'2022 [[paper]](https://arxiv.org/pdf/2203.13241.pdf)

- REGTR: End-to-end Point Cloud Correspondences with Transformers. CVPR'2022 [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Yew_REGTR_End-to-End_Point_Cloud_Correspondences_With_Transformers_CVPR_2022_paper.pdf) [[code]](https://github.com/yewzijian/RegTR)

- UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration. CGF'2022 [[paper]](https://arxiv.org/pdf/2208.02712.pdf) [[code]](https://github.com/ZhileiChen99/UTOPIC)

### Randomized 

- RANSAC: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. 1981 [[paper]](http://www.cs.ait.ac.th/~mdailey/cvreadings/Fischler-RANSAC.pdf)

- 4PCS: 4-points Congruent Sets for Robust Pairwise Surface Registration. TOG'2008 [[paper]](http://www.cs.bgu.ac.il/~aiger/a85-aiger.pdf)

- Model Globally, Match Locally: Efficient and Robust 3D Object Recognition. CVPR'2010 [[paper]](http://campar.cs.tum.edu/pub/drost2010CVPR/drost2010CVPR.pdf) [[code]](https://github.com/adrelino/ppf-reconstruction)

- Super 4PCS: Fast Global Pointcloud Registration via Smart Indexing. CGF'2014 [[paper]](https://hal.archives-ouvertes.fr/hal-01538738/file/super4pcs.pdf) [[code]](https://github.com/nmellado/Super4PCS)

### Probabilistic

- Point Set Registration: Coherent Point Drift. TPAMI'2010 [[paper]](https://arxiv.org/pdf/0905.2635.pdf) [[code]](https://github.com/neka-nat/probreg)

- Robust Point Set Registration Using Gaussian Mixture Models. TPAMI'2011 [[paper]](https://github.com/bing-jian/gmmreg/blob/master/gmmreg_PAMI_preprint.pdf) [[code]](https://github.com/bing-jian/gmmreg)

- A Generative Model for the Joint Registration of Multiple Point Sets. ECCV'2014 [[paper]](http://hal.inria.fr/docs/01/05/45/69/PDF/main_cr.pdf)

- Aligning the Dissimilar: A Probabilistic Method for Feature-based Point Set Registration. ICPR'2016 [[paper]](http://liu.diva-portal.org/smash/get/diva2:1104306/FULLTEXT01)

- A Probabilistic Framework for Color-Based Point Set Registration. CVPR'2016 [[paper]](http://openaccess.thecvf.com/content_cvpr_2016/papers/Danelljan_A_Probabilistic_Framework_CVPR_2016_paper.pdf)

- Density Adaptive Point Set Registration. CVPR'2018 [[paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Lawin_Density_Adaptive_Point_CVPR_2018_paper.pdf) [[code]](https://github.com/felja633/DARE)

- HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration. ECCV'2018 [[paper]](http://jankautz.com/publications/hGMM_ECCV18.pdf)

- Robust Feature-Based Point Registration Using Directional Mixture Model. arxiv'2019 [[paper]](https://arxiv.org/pdf/1912.05016.pdf)

- FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization. CVPR'2019 [[paper]](https://arxiv.org/abs/1811.10136) [[code]](https://bitbucket.org/gaowei19951004/poser/src/master/)

- PointGMM: a Neural GMM Network for Point Clouds. CVPR'2020 [[paper]](https://arxiv.org/abs/2003.13326) [[code]](https://github.com/amirhertz/pointgmm)

- DeepGMR: Learning Latent Gaussian Mixture Models for Registration. ECCV'2020 [[paper]](https://arxiv.org/abs/2008.09088) [[code]](https://github.com/wentaoyuan/deepgmr)

- Registration Loss Learning for Deep Probabilistic Point Set Registration. 3DV'2020 [[paper]](https://arxiv.org/abs/2011.02229) [[code]](https://github.com/felja633/RLLReg)

- A Termination Criterion for Probabilistic PointClouds Registration. arxiv'2020 [[paper]](https://arxiv.org/pdf/2010.04979.pdf)

- LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration. ICCV'2021 [[paper]](https://arxiv.org/pdf/2103.15039.pdf)

### Others

- Gravitational Approach for Point Set Registration. CVPR'2016 [[paper]](https://openaccess.thecvf.com/content_cvpr_2016/papers/Golyanik_Gravitational_Approach_for_CVPR_2016_paper.pdf)

- Accelerated Gravitational Point Set Alignment with Altered Physical Laws. ICCV'2019 [[paper]](https://people.mpi-inf.mpg.de/~golyanik/04_DRAFTS/Golyanik_etal_ICCV_2019.pdf)

- Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations. arxiv'2020 [[paper]](https://arxiv.org/abs/2009.14005)

- Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment. CVPR'2019 [[paper]](http://arxiv.org/pdf/1904.03941)

- Minimal Solvers for 3D Scan Alignment With Pairs of Intersecting Lines. CVPR'2020 [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Mateus_Minimal_Solvers_for_3D_Scan_Alignment_With_Pairs_of_Intersecting_CVPR_2020_paper.pdf)

- Learning multiview 3D point cloud registration. CVPR'2020 [[paper]](https://arxiv.org/abs/2001.05119) [[code]](https://github.com/zgojcic/3D_multiview_reg)

- A Dynamical Perspective on Point Cloud Registration. arxiv'2020 [[paper]](https://arxiv.org/abs/2005.03190)

- Plane Pair Matching for Efficient 3D View Registration. arxiv'2020 [[paper]](https://arxiv.org/pdf/2001.07058.pdf)

- On Bundle Adjustment for Multiview Point Cloud Registration. RA-L'2021 [[paper]](https://arxiv.org/pdf/2108.02976.pdf)

- Provably Approximated Point Cloud Registration. ICCV'2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Jubran_Provably_Approximated_Point_Cloud_Registration_ICCV_2021_paper.pdf)

- GenReg: Deep Generative Method for Fast Point Cloud Registration. arxiv'2021 [[paper]](https://arxiv.org/abs/2111.11783)

- Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization. arxiv'2021 [[paper]](https://arxiv.org/abs/2111.12878)

- DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration. BMVC'2021 [[paper]](https://arxiv.org/abs/2112.09938)

- Deterministic Point Cloud Registration via Novel Transformation Decomposition. CVPR'2022 [[paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.pdf)

## Fine Registration

The fine registration methods (or local registration) produce highly precise registration results, given the initial pose between two point clouds. 

### Traditional 

- Point2Point ICP: A Method for Registration of 3-D Shapes. TPAMI'1992 [[paper]](https://ieeexplore.ieee.org/document/121791)

- Point2Plane Object Modelling by Registration of Multiple Range Images. TPAMI'1992 [[paper]](http://www.cs.hunter.cuny.edu/~ioannis/chen_medioni_point_plane_1991.pdf)

- RPM: New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. [[paper]](http://cmp.felk.cvut.cz/~amavemig/softassign.pdf)

- Matching of 3-D Curves using Semi-differential Invariants. ICCV'1995 [[paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=466913)

- The Trimmed Iterative Closest Point Algorithm. 2002 [[paper]](http://www.inf.u-szeged.hu/ssip/2002/download/Chetverikov.pdf)

- Comparing ICP Variants on Real-World Data Sets. 2013 [[paper]](https://hal.archives-ouvertes.fr/hal-01143458/file/2013_Pomerleau_AutonomousRobots_Comparing.pdf)

- Generalized-ICP. RSS'2009 [[paper]](https://doi.org/10.15607%2Frss.2009.v.021) [[code]](https://github.com/avsegal/gicp)

- Go-ICP: Solving 3D Registration Efficiently and Globally Optimally. ICCV'2013 [[paper]](http://jlyang.org/iccv13_go-icp.pdf) [[code]](https://github.com/yangjiaolong/Go-ICP)

- Colored Point Cloud Registration Revisited. ICCV'2017 [[paper]](https://openaccess.thecvf.com/content_ICCV_2017/papers/Park_Colored_Point_Cloud_ICCV_2017_paper.pdf)

- AA-ICP: Iterative Closest point with Anderson Acceleration. ICRA'2018 [[paper]](https://arxiv.org/abs/1709.05479#:~:text=Iterative%20Closest%20Point%20(ICP)%20is,performing%20scan%2Dmatching%20and%20registration.&text=This%20method%20is%20based%20on,fixed%20point%20of%20contractive%20mapping.)

- Point Clouds Registration with Probabilistic Data Association. IROS'2016 [[paper]](https://github.com/ethz-asl/ipda/wiki/0383.pdf) [[code]](https://github.com/ethz-asl/robust_point_cloud_registration)

- GH-ICP：Iterative Closest Point Algorithm with Global Optimal Matching and Hybrid Metric. 3DV'2018 [[paper]](https://ieeexplore.ieee.org/abstract/document/8490968) [[code]](https://github.com/YuePanEdward/GH-ICP)

- NDT: The Normal Distributions Transform: A New Approach To Laser Scan Matching. IROS'2003 [[paper]](http://hdl.handle.net/10068/262019)

- Best Buddies Registration for Point Clouds. ACCV'2020 [[paper]](https://openaccess.thecvf.com/content/ACCV2020/papers/Drory_Best_Buddies_Registration_for_Point_Clouds_ACCV_2020_paper.pdf)

- Provably Approximated ICP. arxiv'2021 [[paper]](https://arxiv.org/pdf/2101.03588.pdf)

### Learning-based

- DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration. ICCV'2019 [[paper]](https://arxiv.org/abs/1905.04153)

## Datasets

- [Standford 3DScanning](http://graphics.stanford.edu/data/3Dscanrep/)

- [3DMatch](http://3dmatch.cs.princeton.edu/)

- [ETH (Challenging data sets for point cloud registration algorithms)](https://projects.asl.ethz.ch/datasets/doku.php?id=laserregistration:laserregistration)

- [KITTI Odometry](http://www.cvlibs.net/datasets/kitti/eval_odometry.php)

- [ModelNet](https://modelnet.cs.princeton.edu/)

- [A Benchmark for Point Clouds Registration Algorithms](https://github.com/iralabdisco/point_clouds_registration_benchmark)

- [WHU-TLS Benchmark](http://3s.whu.edu.cn/ybs/en/benchmark.htm)

- [LiDAR-Registration-Benchmark](https://github.com/HKUST-Aerial-Robotics/LiDAR-Registration-Benchmark)

## Tools

- [Open3D: A Modern Libray for 3D Data Processing](http://www.open3d.org/docs/release/index.html)

- [PCL: Point Cloud Library](https://pointclouds.org/)

- [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric)

- [PyTorch Points 3D](https://github.com/nicolas-chaulet/torch-points3d)

- [probreg](https://github.com/neka-nat/probreg)