PointCloud-Registration-Tracing

Inspired by awesome-point-cloud-registration and awesome-computer-vision.

This list focuses on the rigid registration between point clouds and tracks progress in point cloud registration research.

Table of Contents

1.Overview
2.Coarse Registration (Global Registration)
- 2.1 Feature Matching Based
  - 2.1.1 Feature Description
  - 2.1.2 Keypoint Detection
  - 2.1.3 Outlier Rejection
  - 2.1.4 Graph Algorithms
- 2.2 End-to-End
- 2.3 Randomized
- 2.4 Probablistic
3.Fine Registration (Local Registration)
- 3.1 Learning-based
- 3.2 Traditional
4.Datasets
5.Tools

1.Overview

Deep learning based point cloud registration: an overview 【VRIH, 2020】
A comprehensive survey on point cloud registration 【arXiv, 2021】
Transformers in 3D Point Clouds: A Survey 【arXiv, 2022】
A review of non-rigid transformations and learning-based 3D point cloud registration methods【ISPRS, 2023】

2.Coarse Registration

2.1 Feature Matching Based

2.1.1 Feature Description

(1) Point-based Strategy

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching【CVPR, 2018】
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors【ECCV, 2018】【code】
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration【ECCV, 2018】【code】
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features 【CVPR, 2020】【code】

(2) Voxel-based Strategy

3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions【CVPR, 2017】【code】
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities【CVPR, 2019] 【code】
FCGF: Fully Convolutional Geometric Features 【ICCV, 2019】【code】

(3) Point-Voxel Strategy

(4) Line-based Strategy

SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud【ECCV, 2022】【code】

(5) Transformer-based Strategy

Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction【arXiv, 2021】
Geometric Transformer for Fast and Robust Point Cloud Registration【CVPR, 2022】【code】

(6) Other Strategy

SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration【CVPR, 2021】【code】
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors【ICCV, 2021】【code】

2.1.2 Keypoint Detection

USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds【ICCV, 2019】【code】
Point cloud saliency detection by local and global feature fusion【T-IP, 2019】
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features 【CVPR, 2020】【code】
3D3L: Deep learned 3D keypoint detection and description for lidars【ICRA, 2021】【code】
UKPGAN: A General Self-Supervised Keypoint Detector【CVPR, 2022】【code】

2.1.3 Outlier Rejection

RANSAC: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography 【1981】
TEASER: Fast and Certifiable Point Cloud Registration 【T-RO, 2020】【code】
SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration【CVPR, 2022】【code】

3.Fine Registration (Local Registration)

4.Datasets

5.Tools