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 *
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Think Bayes 2 by Allen B. Downey The HTML version of this book is here. Think Bayes is an introduction to Bayesian statistics using computational meth

Allen Downey 1.5k Jan 08, 2023
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

MIND 478 Jan 01, 2023
Music source separation is a task to separate audio recordings into individual sources

Music Source Separation Music source separation is a task to separate audio recordings into individual sources. This repository is an PyTorch implmeme

Bytedance Inc. 958 Jan 03, 2023
System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Validating Simulations of User Query Variants This repository contains the scripts of the experiments and evaluations, simulated queries, as well as t

IR Group at Technische Hochschule Köln 2 Nov 23, 2022
Pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model'

RTK-PAD This is an official pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model', which is accepted by IEEE T

6 Aug 01, 2022
Distinguishing Commercial from Editorial Content in News

Distinguishing Commercial from Editorial Content in News In this repository you can find the following: An anonymized version of the data used for my

Timo Kats 3 Sep 26, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
这是一个yolox-pytorch的源码,可以用于训练自己的模型。

YOLOX:You Only Look Once目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤

Bubbliiiing 613 Jan 05, 2023
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
AI Virtual Calculator: This is a simple virtual calculator based on Artificial intelligence.

AI Virtual Calculator: This is a simple virtual calculator that works with gestures using OpenCV. We will use our hand in the air to click on the calc

Md. Rakibul Islam 1 Jan 13, 2022
Pytorch implementation of the paper: "A Unified Framework for Separating Superimposed Images", in CVPR 2020.

Deep Adversarial Decomposition PDF | Supp | 1min-DemoVideo Pytorch implementation of the paper: "Deep Adversarial Decomposition: A Unified Framework f

Zhengxia Zou 72 Dec 18, 2022
Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

SA-AutoAug Scale-aware Automatic Augmentation for Object Detection Yukang Chen, Yanwei Li, Tao Kong, Lu Qi, Ruihang Chu, Lei Li, Jiaya Jia [Paper] [Bi

DV Lab 182 Dec 29, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers

hierarchical-transformer-1d Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers In Progress!! 2021.

MyungHoon Jin 7 Nov 06, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
Official code for "Decoupling Zero-Shot Semantic Segmentation"

Decoupling Zero-Shot Semantic Segmentation This is the official code for the arxiv. ZegFormer is the first framework that decouple the zero-shot seman

Jian Ding 108 Dec 30, 2022
Safe Policy Optimization with Local Features

Safe Policy Optimization with Local Feature (SPO-LF) This is the source-code for implementing the algorithms in the paper "Safe Policy Optimization wi

Akifumi Wachi 6 Jun 05, 2022
🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗

🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗 This year's first semester Club Info challenge will put you at the head of a car racing

ClubINFO INGI (UCLouvain) 6 Dec 10, 2021
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022