# CFNet: LiDAR-Camera Registration Using Calibration Flow Network

arXiv: https://arxiv.org/abs/2104.11907 · HTML: https://mlsanity.com/paper/2104.11907/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2021.

## Built on by (most cited first, 11 of 11)

- [Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching](https://mlsanity.com/paper/2312.04060/) (2023)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [PnLCalib: Sports Field Registration via Points and Lines Optimization](https://mlsanity.com/paper/2404.08401/) (2024)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network](https://mlsanity.com/paper/2505.22427/) (2025)
- [End-to-End Lidar-Camera Self-Calibration for Autonomous Vehicles](https://mlsanity.com/paper/2304.12412/) (2023)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations](https://mlsanity.com/paper/2504.01416/) (2025)
- [Online LiDAR-Camera Extrinsic Parameters Self-checking](https://mlsanity.com/paper/2210.10537/) (2022)
- [Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applications](https://mlsanity.com/paper/2304.07745/) (2023)

## Abstract (arXiv)

As an essential procedure of data fusion, LiDAR-camera calibration is critical for autonomous vehicles and robot navigation. Most calibration methods rely on hand-crafted features and require significant amounts of extracted features or specific calibration targets. With the development of deep learning (DL) techniques, some attempts take advantage of convolutional neural networks (CNNs) to regress the 6 degrees of freedom (DOF) extrinsic parameters. Nevertheless, the performance of these DL-based methods is reported to be worse than the non-DL methods. This paper proposed an online LiDAR-camera extrinsic calibration algorithm that combines the DL and the geometry methods. We define a two-channel image named calibration flow to illustrate the deviation from the initial projection to the ground truth. EPnP algorithm within the RANdom SAmple Consensus (RANSAC) scheme is applied to estimate the extrinsic parameters with 2D-3D correspondences constructed by the calibration flow. Experiments on KITTI datasets demonstrate that our proposed method is superior to the state-of-the-art methods. Furthermore, we propose a semantic initialization algorithm with the introduction of instance centroids (ICs). The code will be publicly available at https://github.com/LvXudong-HIT/CFNet.