# Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences

arXiv: https://arxiv.org/abs/2605.23397 · HTML: https://mlsanity.com/paper/2605.23397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 14 of 14)

- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [MINIMA: Modality Invariant Image Matching](https://mlsanity.com/paper/2412.19412/) (2024)
- [BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation](https://mlsanity.com/paper/2403.11761/) (2024)
- [Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2202.13708/) (2022)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps](https://mlsanity.com/paper/2004.13795/) (2020)
- [MATCHA:Towards Matching Anything](https://mlsanity.com/paper/2501.14945/) (2025)
- [Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections](https://mlsanity.com/paper/2209.07393/) (2022)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences](https://mlsanity.com/paper/2404.17298/) (2024)
- [UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation](https://mlsanity.com/paper/2602.19349/) (2026)
- [Joint Intrinsic and Extrinsic LiDAR-Camera Calibration in Targetless Environments Using Plane-Constrained Bundle Adjustment](https://mlsanity.com/paper/2308.12629/) (2023)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)

## Abstract (arXiv)

Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point correspondences achieve remarkable performance for extrinsic calibration but assume rectified images with known intrinsics. In this work, we overcome this limitation and present the first fully target-less pipeline that jointly estimates camera intrinsics (pinhole model with radial-tangential distortion) and camera-LiDAR extrinsics with deep pixel-point correspondences. Our approach extends deep correspondence-based calibration by (i) automatic intrinsic initialization via structure-from-motion, (ii) generalizing camera-LiDAR matching to raw images with unknown intrinsics including distortion, and (iii) tightly coupling correspondence estimation with joint nonlinear optimization over both intrinsics and extrinsics. We evaluate our method on the KITTI dataset with unseen camera-LiDAR pairs and demonstrate that joint calibration achieves improved extrinsic accuracy while additionally recovering accurate intrinsics.