# LiDAR-Camera Calibration using 3D-3D Point correspondences

arXiv: https://arxiv.org/abs/1705.09785 · HTML: https://mlsanity.com/paper/1705.09785/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 18 of 18)

- [Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving](https://mlsanity.com/paper/2310.07602/) (2023)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios](https://mlsanity.com/paper/2506.16371/) (2025)
- [Optimising the selection of samples for robust lidar camera calibration](https://mlsanity.com/paper/2103.12287/) (2021)
- [DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications](https://mlsanity.com/paper/2406.10068/) (2024)
- [Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving](https://mlsanity.com/paper/2311.10261/) (2023)
- [GSOT3D: Towards Generic 3D Single Object Tracking in the Wild](https://mlsanity.com/paper/2412.02129/) (2024)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion](https://mlsanity.com/paper/2411.10936/) (2024)
- [UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations](https://mlsanity.com/paper/2504.01416/) (2025)
- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)
- [CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth](https://mlsanity.com/paper/2512.14001/) (2025)
- [Towards Visual Query Localization in the 3D World](https://mlsanity.com/paper/2605.01498/) (2026)
- [Optimal Target Shape for LiDAR Pose Estimation](https://mlsanity.com/paper/2109.01181/) (2021)
- [IRisPath: Enhancing Costmap for Off-Road Navigation with Robust IR-RGB Fusion for Improved Day and Night Traversability](https://mlsanity.com/paper/2412.03173/) (2024)
- [Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion](https://mlsanity.com/paper/2506.14706/) (2025)
- [LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration](https://mlsanity.com/paper/2602.21754/) (2026)

## Abstract (arXiv)

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package, more information about which can be sought here: https://github.com/ankitdhall/lidar_camera_calibration .