A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

Overview

Awesome-LiDAR-Camera-Calibration

Awesome

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes.

Outline

0. Introduction

For applications such as autonomous driving, robotics, navigation systems, and 3-D scene reconstruction, data of the same scene is often captured using both lidar and camera sensors. To accurately interpret the objects in a scene, it is necessary to fuse the lidar and the camera outputs together. Lidar camera calibration estimates a rigid transformation matrix (extrinsics, rotation+translation, 6 DoF) that establishes the correspondences between the points in the 3-D lidar plane and the pixels in the image plane.

Example

1. Target-based methods

Paper Target Feature Optimization Toolbox Note
Extrinsic Calibration of a Camera and Laser Range Finder (improves camera calibration), 2004 checkerboard C:Plane (a), L: pts in plane (m) point-to-plane CamLaserCalibraTool CN
Fast Extrinsic Calibration of a Laser Rangefinder to a Camera, 2005 checkerboard C: Plane (a), L: Plane (m) plane(n/d) correspondence, point-to-plane LCCT *
Extrinsic calibration of a 3D laser scanner and an omnidirectional camera, 2010 checkerboard C: plane (a), L: pts in plane (m) point-to-plane cam_lidar_calib *
LiDAR-Camera Calibration using 3D-3D Point correspondences, 2017 cardboard + ArUco C: 3D corners (a), L: 3D corners (m) ICP lidar_camera_calibration *
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard, 2017 checkerboard C: 2D corners (a), L: 3D corners (a) PnP, angle difference ILCC *
Extrinsic Calibration of Lidar and Camera with Polygon, 2018 regular cardboard C: 2D edge, corners (a), L: 3D edge, pts in plane (a) point-to-line, point-inside-polygon ram-lab/plycal *
Automatic Extrinsic Calibration of a Camera and a 3D LiDAR using Line and Plane Correspondences, 2018 checkerboard C: 3D edge, plane(a), L: 3D edge, pts in plane (a) direcion/normal, point-to-line, point-to-plane Matlab LiDAR Toolbox *
Improvements to Target-Based 3D LiDAR to Camera Calibration, 2020 cardboard with ArUco C: 2d corners (a), L: 3D corners (a) PnP, IOU github *
ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems, 2020 checkerboard C: 2D corners (a), L: 3D corners (a) PnP ACSC *
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups, 2021 cardboard with circle & Aruco C: 3D points (a), L: 3D points (a) ICP velo2cam_ calibration *

C: camera, L: LiDAR, a: automaic, m: manual

2. Targetless methods

2.1. Motion-based methods

Paper Feature Optimization Toolbox Note
LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry, 2018 C: motion (ICP), L: motion (VO) hand-eye calibration * *

2.2. Scene-based methods

2.2.1. Traditional methods

Paper Feature Optimization Toolbox Note
Automatic Targetless Extrinsic Calibration of a 3D Lidar and Camera by Maximizing Mutual Information, 2012 C:grayscale, L: reflectivity mutual information, BB steepest gradient ascent Extrinsic Calib *
Automatic Calibration of Lidar and Camera Images using Normalized Mutual Information, 2013 C:grayscale, L: reflectivity, noraml normalized MI, particle swarm * *
Automatic Online Calibration of Cameras and Lasers, 2013 C: Canny edge, L: depth-discontinuous edge correlation, grid search * *
SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera, 2020 semantic centroid PnP * *
A Low-cost and Accurate Lidar-assisted Visual SLAM System, 2021 C: edge(grayscale), L: edge (reflectivity, depth projection) ICP, coordinate descent algorithms CamVox *
Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments,2021 C:Canny edge(grayscale), L: depth-continuous edge point-to-line, Gaussian-Newton livox_camera_calib *
CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes, 2021 C:straight line, L: straight line perspective3-lines (P3L) * CN

2.2.2. Deep-learning methods

Pape Toolbox Note
RegNet: Multimodal sensor registration using deep neural networks, 2017,IV regnet *
CalibNet: Geometrically supervised extrinsic calibration using 3d spatial transformer networks,2018,IROS CalibNet *

3. Other toolboxes

Toolbox Introduction Note
Apollo sensor calibration tools targetless method, no source code CN
Autoware camera lidar calibrator pick points mannually, PnP *
Autoware calibration camera lidar checkerboard, similar to LCCT CN
livox_camera_lidar_calibration pick points mannually, PnP *
nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation "

nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation ". Please

jsguo 610 Dec 28, 2022
Turi Create simplifies the development of custom machine learning models.

Quick Links: Installation | Documentation | WWDC 2019 | WWDC 2018 Turi Create Check out our talks at WWDC 2019 and at WWDC 2018! Turi Create simplifie

Apple 10.9k Jan 01, 2023
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Lightweight (Bayesian) Media Mix Model This is not an official Google product. L

Google 342 Jan 03, 2023
Semi-supervised Implicit Scene Completion from Sparse LiDAR

Semi-supervised Implicit Scene Completion from Sparse LiDAR Paper Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZH

114 Nov 30, 2022
Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Implementation of Neural Scene Graphs, that optimizes multiple radiance fields to represent different objects and a static scene background. Learned representations can be rendered with novel object

151 Dec 26, 2022
Official implementation of Densely connected normalizing flows

Densely connected normalizing flows This repository is the official implementation of NeurIPS 2021 paper Densely connected normalizing flows. Poster a

Matej Grcić 31 Dec 12, 2022
Generic Event Boundary Detection: A Benchmark for Event Segmentation

Generic Event Boundary Detection: A Benchmark for Event Segmentation We release our data annotation & baseline codes for detecting generic event bound

47 Nov 22, 2022
MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity

MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity Introduction The 3D LiDAR place recognition aim

16 Dec 08, 2022
Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models

Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models This repo contains a barebones implementation for the atta

16 Dec 04, 2022
Data Augmentation with Variational Autoencoders

Documentation Pyraug This library provides a way to perform Data Augmentation using Variational Autoencoders in a reliable way even in challenging con

112 Nov 30, 2022
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas Fürst* 1, Elisabeth Rumetshofer* 1, Viet Tran1, Hubert Ramsauer1, Fei Tang3, Joh

Institute for Machine Learning, Johannes Kepler University Linz 133 Jan 04, 2023
Implementation of ResMLP, an all MLP solution to image classification, in Pytorch

ResMLP - Pytorch Implementation of ResMLP, an all MLP solution to image classification out of Facebook AI, in Pytorch Install $ pip install res-mlp-py

Phil Wang 178 Dec 02, 2022
A collection of 100 Deep Learning images and visualizations

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

AI Summer 65 Sep 12, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:

MLJAR Automated Machine Learning Documentation: https://supervised.mljar.com/ Source Code: https://github.com/mljar/mljar-supervised Table of Contents

MLJAR 2.4k Dec 31, 2022
gACSON software for visualization, processing and analysis of three-dimensional electron microscopy images

gACSON gACSON software is to visualize, segment, and analyze the morphology of neurons in three-dimensional electron microscopy images. If you use any

Andrea Behanova 2 May 31, 2022
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Introduction This repository contains the modified caffe library and network architectures for our paper "Automated Melanoma Recognition in Dermoscopy

Lequan Yu 47 Nov 24, 2022
The implementation of the lifelong infinite mixture model

Lifelong infinite mixture model 📋 This is the implementation of the Lifelong infinite mixture model 📋 Accepted by ICCV 2021 Title : Lifelong Infinit

Fei Ye 5 Oct 20, 2022