In this paper, we address extrinsic calibration for camera, lidar, and 4D radar sensors. Accurate extrinsic calibration of radar remains a challenge due to the sparsity of its data. We propose CLRNet, a novel, multi-modal end-to-end deep learning (DL) calibration network capable of addressing joint camera-lidar-radar calibration, or pairwise calibration between any two of these sensors. We incorporate equirectangular projection, camera-based depth image prediction, additional radar channels, and leverage lidar with a shared feature space and loop closure loss. In extensive experiments using the View-of-Delft and Dual-Radar datasets, we demonstrate superior calibration accuracy compared to existing state-of-the-art methods, reducing both median translational and rotational calibration errors by at least 50%. Finally, we examine the domain transfer capabilities of the proposed network and baselines, when evaluating across datasets. The code will be made publicly available upon acceptance at: https://github.com/tudelft-iv.
@article{arxiv.2603.15767,
title = {CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning},
author = {Marcell Kegl and Andras Palffy and Csaba Benedek and Dariu M. Gavrila},
journal= {arXiv preprint arXiv:2603.15767},
year = {2026}
}
Comments
Submitted to IEEE Transactions on Intelligent Vehicles