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https://github.com/wsunid/awesome-point-clouds-registration

A list of papers about point clouds registration
https://github.com/wsunid/awesome-point-clouds-registration
List: awesome-point-clouds-registration
3d-vision point-cloud-registration
Last synced: 16 days ago
JSON representation
A list of papers about point clouds registration
Host: GitHub
URL: https://github.com/wsunid/awesome-point-clouds-registration
Owner: wsunid
Created: 2019-08-09T15:38:01.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-12-06T00:41:44.000Z (about 1 year ago)
Last Synced: 2024-05-22T15:12:15.920Z (7 months ago)
Topics: 3d-vision, point-cloud-registration
Homepage:
Size: 37.1 KB
Stars: 382
Watchers: 24
Forks: 78
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-point-clouds-registration - A list of papers about point clouds registration. (Other Lists / Monkey C Lists)
README

        # awesome-point-cloud-registration

A curated list of papers about point cloud registration inspired by [awesome point cloud analysis](https://github.com/Yochengliu/awesome-point-cloud-analysis)

You will be very welcome to make PR and contribute!! :smile:

 Keywords 


__`lf.`__: local features for registration   |  

__`corr.`__: register with putative correspondences   |  

__`est.`__: direct estimation   |  

__`dat.`__: datasets   |   

__`opt.`__: optimization   |   

__`oth.`__: other

 Statistics 
 

 

:fire: code is available & stars >= 100  |  :star: citation >= 50

--- 

## - 2014

- [[CGF](https://dl.acm.org/doi/10.1111/cgf.12446)] SUPER 4PCS: Fast Global Pointcloud Registration via Smart Indexing. [[code](https://github.com/STORM-IRIT/OpenGR)] [`est.` `oth.`] :fire: :star:

## - 2016

- [[CGF](https://dl.acm.org/citation.cfm?id=2600305)] Sparse Iterative Closest Point. [[code](https://github.com/OpenGP/sparseicp)] [`est.` `opt.`] :fire: :star: 

- [[IJRR](https://hal.archives-ouvertes.fr/hal-01143454/document)] Challenging data sets for point cloud registration algorithms. [[code](https://projects.asl.ethz.ch/datasets/doku.php?id=laserregistration:laserregistration)] [`dat.`] :star:

## 2017

- [[CVPR](https://arxiv.org/pdf/1603.08182.pdf)] 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions. [[code](https://github.com/andyzeng/3dmatch-toolbox)] [`lf.` `dat.`] :fire: :star:

- [[CVPR](https://zpascal.net/cvpr2017/Elbaz_3D_Point_Cloud_CVPR_2017_paper.pdf)] 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder. [[code](https://github.com/gilbaz/LORAX)] [`est.`]

- [[ICCV](https://arxiv.org/pdf/1709.05056.pdf)] Learning Compact Geometric Features. [[code](https://github.com/marckhoury/CGF)] [`est.` `dat.`]

## 2018

- [[ECCV](https://eccv2018.org/openaccess/content_ECCV_2018/papers/Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf)] 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [[code](https://github.com/yewzijian/3DFeatNet)] [`lf.`]

- [[ECCV](http://openaccess.thecvf.com/content_ECCV_2018/papers/Tolga_Birdal_PPF-FoldNet_Unsupervised_Learning_ECCV_2018_paper.pdf)] PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors [`lf.`]

- [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_PPFNet_Global_Context_CVPR_2018_paper.pdf)] PPFNet: Global Context Aware Local Features for Robust 3D Point Matching [`lf.`]

- [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Georgakis_End-to-End_Learning_of_CVPR_2018_paper.pdf)] End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching [`lf.`]

- [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Vongkulbhisal_Inverse_Composition_Discriminative_CVPR_2018_paper.pdf)] Inverse Composition Discriminative Optimization for Point Cloud Registration [`opt.`]

## 2019

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Aoki_PointNetLK_Robust__Efficient_Point_Cloud_Registration_Using_PointNet_CVPR_2019_paper.pdf)] PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. [[code](https://github.com/hmgoforth/PointNetLK)] [`est.`] :fire:

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_DeepMapping_Unsupervised_Map_Estimation_From_Multiple_Point_Clouds_CVPR_2019_paper.pdf)] DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds. [[code](https://github.com/ai4ce/DeepMapping)] [`est.` `opt.`] :fire:

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Gojcic_The_Perfect_Match_3D_Point_Cloud_Matching_With_Smoothed_Densities_CVPR_2019_paper.pdf)] The Perfect Match: 3D Point Cloud Matching with Smoothed Densities [[code](https://github.com/zgojcic/3DSmoothNet)] [`lf.`]

- [[CVPR](https://songshiyu01.github.io/pdf/L3Net_W.Lu_Y.Zhou_S.Song_CVPR2019.pdf)] L3 -Net: Towards Learning based LiDAR Localization for Autonomous Driving [`lf.` `est.`]

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Le_SDRSAC_SemidefiniteBased_Randomized_Approach_for_Robust_Point_Cloud_Registration_Without_CVPR_2019_paper.pdf)] SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences [[code](https://github.com/intellhave/SDRSAC)] [ `est.` `opt.`]

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_3D_Local_Features_for_Direct_Pairwise_Registration_CVPR_2019_paper.pdf)] 3D Local Features for Direct Pairwise Registration [`lf.` `est.`]

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.pdf)] 3D Point Capsule Networks [[code](https://github.com/yongheng1991/3D-point-capsule-networks)] [`lf.`] :fire:

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Melzi_GFrames_Gradient-Based_Local_Reference_Frame_for_3D_Shape_Matching_CVPR_2019_paper.pdf)] GFrames: Gradient-Based Local Reference Frame

for 3D Shape Matching [`oth.`]

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2019/papers/Gao_FilterReg_Robust_and_Efficient_Probabilistic_Point-Set_Registration_Using_Gaussian_Filter_CVPR_2019_paper.pdf)] FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization [[code](https://bitbucket.org/gaowei19951004/poser/src/master/)]  [`opt.` `oth.`]

- [[ICCV](https://songshiyu01.github.io/publication/iccv2019_registration/)] DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration [`est.` `lf.`]

- [[ICCV](https://arxiv.org/abs/1904.00229)] Deep Closest Point: Learning Representations for Point Cloud Registration [[code](https://github.com/WangYueFt/dcp)] [`est.`]

- [[ICCV](https://arxiv.org/pdf/1905.03304.pdf)] USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. [[code](https://github.com/lijx10/USIP)] [`lf.`]

- [[ICCV](https://arxiv.org/pdf/1909.06887.pdf)] Learning an Effective Equivariant 3D Descriptor Without Supervision. [`lf.`]

- [[ICCV](https://node1.chrischoy.org/data/publications/fcgf/fcgf.pdf)] Fully Convolutional Geometric Features. [[code](https://github.com/chrischoy/FCGF)][`lf.`]

- [[ICRA](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8793857)] Robust low-overlap 3-D point cloud registration for outlier rejection [`est.` `opt.` `corr.` `oth.`]

- [[ICRA](https://ieeexplore.ieee.org/abstract/document/8793516)] CELLO-3D: Estimating the Covariance of ICP in the Real World [`est.` `opt.` `oth.`]

- [[NeurIPS](https://github.com/weiweisun2018/awesome-point-clouds-registration)] PRNet: Self-Supervised Learning for Partial-to-Partial Registration [`est.`]

- [[TOG](https://gfx.cs.princeton.edu/pubs/Rusinkiewicz_2019_ASO/symm_icp.pdf)] A Symmetric Objective Function for ICP [`est.` `opt.`]

- [[ARXIV](https://arxiv.org/pdf/1904.01701.pdf)] 3DRegNet: A Deep Neural Network for 3D Point Registration [`est.`]

- [[ARXIV](https://arxiv.org/pdf/1910.10328.pdf)] Iterative Matching Point [`est.`]

- [[ARXIV](https://arxiv.org/pdf/1908.07906.pdf)] PCRNet: Point Cloud Registration Network using PointNet Encoding [[code](https://github.com/vinits5/pcrnet)] [`est.`]

## 2020

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

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

- [[CVPR](https://openaccess.thecvf.com/content_CVPR_2020/papers/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.pdf

)] 3DRegNet: A Deep Neural Network for 3D Point Registration [[code](https://github.com/goncalo120/3DRegNet)] [`corr.`]

- [[CVPR](https://arxiv.org/abs/2005.08144)] High-Dimensional Convolutional Networks for Geometric Pattern Recognition [[code](https://github.com/chrischoy/HighDimConvNets)] [`corr.`]

- [[CVPR](https://arxiv.org/abs/2004.11540)] Deep Global Registration [[code](https://github.com/chrischoy/DeepGlobalRegistration)] [`lf.` `corr.`]

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

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

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

- [[CVPR](https://arxiv.org/abs/2003.13326)] PointGMM: a Neural GMM Network for Point Clouds [`est.` `oth.`]

- [[ECCV](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500715.pdf)] DeepGMR: Learning Latent Gaussian Mixture Models for Registration  [[code](https://github.com/wentaoyuan/deepgmr)] [`est.` `corr.`]

- [[ARXIV](https://arxiv.org/abs/2001.07715)] TEASER: Fast and Certifiable Point Cloud Registration. [[code](https://github.com/MIT-SPARK/TEASER-plusplus)] [`corr.` `opt.`]

## 2022

- [[ICRA](https://arxiv.org/abs/2203.06612)] A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments [[code](https://github.com/url-kaist/Quatro)] [`corr.` `opt.`] :fire:

## 2023

- [[Measurement,Elsever](https://www.sciencedirect.com/science/article/abs/pii/S0263224123007637)] Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms [[code](https://github.com/MapsHD/HDMapping)] [`est.` `opt.` `oth.`]

- [[ICRA](https://arxiv.org/abs/2301.07425)] Segregator: Global Point Cloud Registration with Semantic and Geometric Cues [[code](https://github.com/Pamphlett/Segregator)] [`corr.` `opt.`] :fire:

- [[TASE](https://arxiv.org/abs/2308.11573)] G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model [[code](https://github.com/HKUST-Aerial-Robotics/G3Reg)] [`corr.` `opt.`] :fire: