# CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes

arXiv: https://arxiv.org/abs/2103.04558 · HTML: https://mlsanity.com/paper/2103.04558/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry](https://mlsanity.com/paper/1804.05178/) (2018)
- [SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera](https://mlsanity.com/paper/2003.04260/) (2020)
- [Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard](https://mlsanity.com/paper/1708.05514/) (2017)

## Built on by (most cited first, 19 of 19)

- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything](https://mlsanity.com/paper/2306.02656/) (2023)
- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving](https://mlsanity.com/paper/2411.05311/) (2024)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [Environment-Driven Online LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2502.00801/) (2025)
- [SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving](https://mlsanity.com/paper/2304.05530/) (2023)
- [YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems](https://mlsanity.com/paper/2407.18043/) (2024)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors](https://mlsanity.com/paper/2504.01040/) (2025)
- [A re-calibration method for object detection with multi-modal alignment bias in autonomous driving](https://mlsanity.com/paper/2405.16848/) (2024)
- [UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations](https://mlsanity.com/paper/2504.01416/) (2025)
- [CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth](https://mlsanity.com/paper/2512.14001/) (2025)
- [DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network](https://mlsanity.com/paper/2601.01188/) (2026)
- [Online LiDAR-Camera Extrinsic Parameters Self-checking](https://mlsanity.com/paper/2210.10537/) (2022)
- [Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching](https://mlsanity.com/paper/2506.22784/) (2025)
- [A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System](https://mlsanity.com/paper/2507.16621/) (2025)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)

## Abstract (arXiv)

For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems. However, existing calibration techniques require either a complicated setting with various calibration targets, or an initial calibration provided beforehand, which greatly impedes their applicability in large-scale autonomous vehicle deployment. To tackle these issues, we propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and poles in both image and point cloud and formulates the initial calibration of extrinsic parameters as a perspective-3-lines (P3L) problem. Subsequently, a cost function defined under the semantic constraints of the line features is designed to perform refinement on the solved coarse calibration. The whole procedure is fully automatic and user-friendly without the need to adjust environment settings or provide an initial calibration. We conduct extensive experiments on KITTI and our in-house dataset, quantitative and qualitative results demonstrate the robustness and accuracy of our method.