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A curated list of point cloud registration.

awesome-point-cloud-registration

A curated list of resources on point cloud registration inspired by 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)
    • Feature Matching Based
      • Keypoint Detection
      • Feature Description
      • Outlier Rejection
      • Graph Algorithms
    • End-to-End
    • Randomized
    • Probablistic
  • Fine Registration (Local Registration)
    • Learning-based
    • Traditional
  • Datasets
  • 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]
  • Harris3D: a robust extension of the harris operator for interest point detection on 3D meshes. VC'2011 [paper]
  • Intrinsic shape signatures: A shape descriptor for 3D object recognition. ICCV'2009 [paper]
  • Learning a Descriptor-Specific 3D Keypoint Detector. ICCV'2015 [paper]
  • 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. ECCV'2018 [paper] [code]
  • USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds. ICCV'2019 [paper] [code]
  • D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. CVPR'2020 [paper] [code]
  • PointCloud Saliency Maps. ICCV'2019 [paper] [code]
  • SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints. AAAI'2020 [paper]
  • SKD: Unsupervised Keypoint Detecting for Point Clouds using Embedded Saliency Estimation. arxiv'2019 [paper]
  • Fuzzy Logic and Histogram of Normal Orientation-based 3D Keypoint Detection For Point Clouds. PRL'2020 [paper]
  • MaskNet: A Fully-Convolutional Network to Estimate Inlier Points. 3DV'2020 [paper] [code]
  • PREDATOR: Registration of 3D Point Clouds with Low Overlap. arxiv'2020 [paper] [code]

Survey:

  • Performance Evaluation of 3D Keypoint Detectors. IJCV'2013 [paper]

Feature Description

  • Spin Image: Using spin images for efficient object recognition in cluttered 3D scenes. TPAMI'1999 [paper]
  • USC: Unique shape context for 3D data description. 3DOR'2010 [paper]
  • 3DShapeContext: Recognizing Objects in Range Data Using Regional Point Descriptors. ECCV'2004 [paper]
  • SHOT: Unique Signatures of Histograms for Local Surface Description. ECCV'2010 [paper]
  • FPFH: Fast Point Feature Histograms (FPFH) for 3D registration. ICRA'2009 [paper]
  • RoPS: 3D Free Form Object Recognition using Rotational Projection Statistics. WACV'2013 [paper]
  • CGF: Learning Compact Geometric Features. ICCV'2017 [paper]
  • 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions. CVPR'2017 [paper] [code]
  • End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching. CVPR'2018 [paper]
  • PPFNet: Global Context Aware Local Features for Robust 3D Point Matching. CVPR'2018 [paper]
  • 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration. ECCV'2018 [paper] [code]
  • MVDesc: Learning and Matching Multi-View Descriptors for Registration of Point Clouds. ECCV'2018 [paper] [code]
  • FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation. CVPR'2018 [paper] [code]
  • PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. ECCV'2018 [paper] [code]
  • 3D Local Features for Direct Pairwise Registration. CVPR'2019 [paper]
  • 3D Point-Capsule Networks. CVPR'2019 [paper] [code]
  • The Perfect Match: 3D Point Cloud Matching with Smoothed Densities. CVPR'2019 [paper] [code]
  • FCGF: Fully Convolutional Geometric Features. ICCV'2019 [paper] [code]
  • Learning an Effective Equivariant 3D Descriptor Without Supervision. ICCV'2019 [paper]
  • D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. CVPR'2020 [paper] [code]
  • End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds. CVPR'2020 [paper] [code]
  • LRF-Net- Learning Local Reference Frames for 3D Local Shape Description and Matching. arxiv'2020 [paper]
  • DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization. ECCV'2020 [paper] [code]
  • Distinctive 3D local deep descriptors. arxiv'2020 [paper] [code]
  • SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration. CVPR'2021 [paper] [code]
  • PREDATOR: Registration of 3D Point Clouds with Low Overlap. CVPR'2021 [paper] [code]
  • Self-supervised Geometric Perception. CVPR'2021 [paper] [code]
  • 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning. arxiv'2021 [paper] [code]
  • Generalisable and Distinctive (GeDi) 3D local deep descriptors for point cloud registration. arxiv'2021 [paper] [code]
  • Neighborhood Normalization for Robust Geometric Feature Learning. CVPR'2021 [paper] [code]
  • UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering. CVPR'2021 [paper] [code]
  • Bootstrap Your Own Correspondences. ICCV'2021 [paper] [code]
  • WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration. TVCG'2022 [paper] [code]
  • You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors. ICCV'2021 [paper] [code]
  • P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching. ICCV'2021 [paper]
  • Distinctiveness oriented Positional Equilibrium for Point Cloud Registration. ICCV'2021 [paper]
  • CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration. NeurIPS'2021 [paper] [code]
  • IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration. arxiv'2021 [paper]
  • Lepard: Learning partial point cloud matching in rigid and deformable scenes. CVPR'2022 [paper] [code]
  • Fast and Robust Registration of Partially Overlapping Point Clouds. RA-L'2021 [paper] [code]
  • Geometric Transformer for Fast and Robust Point Cloud Registration. CVPR'2022 [paper] [code]
  • ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs. SIGGRAPH'2022 [paper]
  • Learning to Register Unbalanced Point Pairs. arxiv'2022 [paper]

Survey:

  • A Comprehensive Performance Evaluation of 3D Local Feature Descriptors. IJCV'2015 [paper]
  • Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching. ICIP'2019 [paper]

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]
  • Locally Optimized RANSAC. 2003 [paper]
  • Graph-cut RANSAC. CVPR'2018 [paper] [code]
  • MAGSAC: Marginalizing Sample Consensus. CVPR'2019 [paper] [code]
  • VFC: A Robust Method for Vector Field Learning with Application To Mismatch Removing. CVPR'2011 [paper]
  • In Search of Inliers: 3D Correspondence by Local and Global Voting. CVPR'2014 [paper]
  • FGR: Fast Global Registration. ECCV'2016 [paper] [code]
  • Ranking 3D Feature Correspondences Via Consistency Voting. PRL'2019 [paper]
  • An Accurate and Efficient Voting Scheme for a Maximally All-Inlier 3D Correspondence Set. TPAMI'2020 [paper]
  • GORE: Guaranteed Outlier Removal for Point Cloud Registration with Correspondences. TPAMI'2018 [paper] [code]
  • A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates. RSS'2019 [paper]
  • Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection. ICRA'2020 [paper]
  • TEASER: Fast and Certifiable Point Cloud Registration. T-RO'2020 [paper] [code]
  • One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers. NeurIPS'2020 [paper]
  • SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences. CVPR'2019 [paper] [code]
  • Robust Low-Overlap 3D Point Cloud Registration for Outlier Rejection. ICRA'2019 [paper]
  • ICOS: Efficient and Highly Robust Rotation Search and Point Cloud Registration with Correspondences. arxiv'2021 [paper]
  • Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration. arxiv'2022 [paper]
  • A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments. ICRA'2022 [paper] [code]
  • SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. CVPR'2022 [paper] [code]

Learning based (including 2D outlier rejection methods)

  • Learning to Find Good Correspondences. CVPR'2018 [paper] [code]
  • NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences. CVPR'2019 [paper] [code]
  • OANet: Learning Two-View Correspondences and Geometry using Order-Aware Network. ICCV'2019 [paper] [code]
  • ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning. CVPR'2020 [paper] [code]
  • SuperGlue: Learning Feature Matching with Graph Neural Networks. CVPR'2020 [paper] [code]
  • 3DRegNet: A Deep Neural Network for 3D Point Registration. CVPR'2020 [paper] [code]
  • Deep Global Registration. CVPR'2020 [paper] [code]
  • 3D Correspondence Grouping with Compatibility Features. arxiv'2020 [paper]
  • PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency. CVPR'2021 [paper] [code]
  • StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks. CVPR'2021 [paper]
  • HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration. ICCV'2021 [paper] [code]
  • Keypoint Matching for Point Cloud Registration using Multiplex Dynamic Graph Attention Networks. RA-L'2021 [paper]
  • Deep Hough Voting for Robust Global Registration. ICCV'2021 [paper]
  • End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration. arxiv'2021 [paper]
  • TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios. arxiv'2021 [paper]
  • COTReg: Coupled Optimal Transport based Point Cloud Registration. arxiv'2021 [paper]
  • DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration. AAAI'2022 [paper] [code]
  • Multi-instance Point Cloud Registration by Efficient Correspondence Clustering. CVPR'2022 [paper] [code]

Survey

  • A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching. TPAMI'2019 [paper]

Graph Algorithms

  • A Graduated Assignment Algorithm for Graph Matching. TPAMI'1996 [paper]
  • A Spectral Technique for Correspondence Problems Using Pairwise Constraints. ICCV'2005 [paper] [code]
  • Balanced Graph Matching. NIPS'2006 [paper] [code]
  • Feature Correspondence via Graph Matching: Models and Global Optimization. ECCV'2008 [paper]
  • An Integer Projected Fixed Point Method for Graph Matching and MAP Inference. NIPS'2009 [paper] [code]
  • Optimal Correspondences From Pairwise Constraints. ICCV'2009 [paper]
  • Reweighted Random Walks for Graph Matching. ECCV'2010 [paper]
  • Maximal Cliques Based Rigid Body Motion Segmentation with a RGB-D Camera. ACCV'2012 [paper]
  • A Probabilistic Approach to Spectral Graph Matching. TPAMI'2013 [paper]
  • A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints. arxiv'2019 [paper]
  • ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants. arxiv'2020 [paper]
  • CLIPPER A Graph-Theoretic Framework for Robust Data Association. arxiv'2020 [paper]
  • PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency CVPR'2021 [paper] [code]
  • Pairwise Point Cloud Registration Using Graph Matching and Rotation-invariant Features. arxiv'2021 [paper]

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] [code]
  • Deep Closest Point: Learning Representations for Point Cloud Registration. ICCV'2019 [paper] [code]
  • PRNet: Self-Supervised Learning for Partial-to-Partial Registration. NeurIPS'2019 [paper] [code]
  • AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects. 3DV'2019 [paper] [code]
  • RPM-Net: Robust Point Matching using Learned Features. CVPR'2020 [paper] [code]
  • Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. CVPR'2020 [code] [code]
  • Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration. ECCV'2020 [paper] [code]
  • Self-supervised Point Set Local Descriptors for Point Cloud Registration. arxiv'2020 [paper]
  • Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration. arxiv'2020 [paper]
  • Robust Point Cloud Registration Framework Based on Deep Graph Matching. CVPR'2021 [paper] [code]
  • RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2^D-Tree Representation. CVPR'2021 [paper]
  • Deep Weighted Consensus (DWC) Dense correspondence confidence maps for 3D shape registration. arxiv'2021 [paper]
  • OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration. arxiv'2021 [paper]
  • FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration. AAAI'2022 [paper] [code]
  • PointNetLK Revisited. CVPR'2021 [paper]
  • Point Cloud Registration using Representative Overlapping Points. arxiv'2021 [paper] [code]
  • Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations. arxiv'2021 [paper]
  • Geometry Guided Network for Point Cloud Registration. RA-L'2021 [paper]
  • (Just) A Spoonful of Refinements Helps the Registration Error Go Down. ICCV'2021 [paper] [code]
  • A Robust Loss for Point Cloud Registration. ICCV'2021 [paper]
  • Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration. ICCV'2021 [paper] [code]
  • DeepBBS: Deep Best Buddies for Point Cloud Registration. 3DV'2021 [paper] [code]
  • PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds. ICCV'2021 [paper] [code]
  • Feature Interactive Representation for Point Cloud Registration. ICCV'2021 [paper]
  • DeepPRO: Deep Partial Point Cloud Registration of Objects. ICCV'2021 [paper]
  • What Stops Learning-based 3D Registration from Working in the Real World? arxiv'2021 [paper]
  • Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction. arxiv'2021 [paper] [code]
  • Reliable Inlier Evaluation for Unsupervised Point Cloud Registration. AAAI'2022 [paper]
  • VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration. arxiv'2022 [paper]
  • REGTR: End-to-end Point Cloud Correspondences with Transformers. CVPR'2022 [paper] [code]
  • UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration. CGF'2022 [paper] [code]

Randomized

  • RANSAC: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. 1981 [paper]
  • 4PCS: 4-points Congruent Sets for Robust Pairwise Surface Registration. TOG'2008 [paper]
  • Model Globally, Match Locally: Efficient and Robust 3D Object Recognition. CVPR'2010 [paper] [code]
  • Super 4PCS: Fast Global Pointcloud Registration via Smart Indexing. CGF'2014 [paper] [code]

Probabilistic

  • Point Set Registration: Coherent Point Drift. TPAMI'2010 [paper] [code]
  • Robust Point Set Registration Using Gaussian Mixture Models. TPAMI'2011 [paper] [code]
  • A Generative Model for the Joint Registration of Multiple Point Sets. ECCV'2014 [paper]
  • Aligning the Dissimilar: A Probabilistic Method for Feature-based Point Set Registration. ICPR'2016 [paper]
  • A Probabilistic Framework for Color-Based Point Set Registration. CVPR'2016 [paper]
  • Density Adaptive Point Set Registration. CVPR'2018 [paper] [code]
  • HGMR: Hierarchical Gaussian Mixtures for Adaptive 3D Registration. ECCV'2018 [paper]
  • Robust Feature-Based Point Registration Using Directional Mixture Model. arxiv'2019 [paper]
  • FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization. CVPR'2019 [paper] [code]
  • PointGMM: a Neural GMM Network for Point Clouds. CVPR'2020 [paper] [code]
  • DeepGMR: Learning Latent Gaussian Mixture Models for Registration. ECCV'2020 [paper] [code]
  • Registration Loss Learning for Deep Probabilistic Point Set Registration. 3DV'2020 [paper] [code]
  • A Termination Criterion for Probabilistic PointClouds Registration. arxiv'2020 [paper]
  • LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration. ICCV'2021 [paper]

Others

  • Gravitational Approach for Point Set Registration. CVPR'2016 [paper]
  • Accelerated Gravitational Point Set Alignment with Altered Physical Laws. ICCV'2019 [paper]
  • Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations. arxiv'2020 [paper]
  • Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment. CVPR'2019 [paper]
  • Minimal Solvers for 3D Scan Alignment With Pairs of Intersecting Lines. CVPR'2020 [paper]
  • Learning multiview 3D point cloud registration. CVPR'2020 [paper] [code]
  • A Dynamical Perspective on Point Cloud Registration. arxiv'2020 [paper]
  • Plane Pair Matching for Efficient 3D View Registration. arxiv'2020 [paper]
  • On Bundle Adjustment for Multiview Point Cloud Registration. RA-L'2021 [paper]
  • Provably Approximated Point Cloud Registration. ICCV'2021 [paper]
  • GenReg: Deep Generative Method for Fast Point Cloud Registration. arxiv'2021 [paper]
  • Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization. arxiv'2021 [paper]
  • DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration. BMVC'2021 [paper]
  • Deterministic Point Cloud Registration via Novel Transformation Decomposition. CVPR'2022 [paper]

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]
  • Point2Plane Object Modelling by Registration of Multiple Range Images. TPAMI'1992 [paper]
  • RPM: New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. [paper]
  • Matching of 3-D Curves using Semi-differential Invariants. ICCV'1995 [paper]
  • The Trimmed Iterative Closest Point Algorithm. 2002 [paper]
  • Comparing ICP Variants on Real-World Data Sets. 2013 [paper]
  • Generalized-ICP. RSS'2009 [paper] [code]
  • Go-ICP: Solving 3D Registration Efficiently and Globally Optimally. ICCV'2013 [paper] [code]
  • Colored Point Cloud Registration Revisited. ICCV'2017 [paper]
  • AA-ICP: Iterative Closest point with Anderson Acceleration. ICRA'2018 [paper]
  • Point Clouds Registration with Probabilistic Data Association. IROS'2016 [paper] [code]
  • GH-ICP：Iterative Closest Point Algorithm with Global Optimal Matching and Hybrid Metric. 3DV'2018 [paper] [code]
  • NDT: The Normal Distributions Transform: A New Approach To Laser Scan Matching. IROS'2003 [paper]
  • Best Buddies Registration for Point Clouds. ACCV'2020 [paper]
  • Provably Approximated ICP. arxiv'2021 [paper]

Learning-based

  • DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration. ICCV'2019 [paper]

Datasets

Tools

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A curated list of point cloud registration.

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