# One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving

arXiv: https://arxiv.org/abs/2511.12291 · HTML: https://mlsanity.com/paper/2511.12291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [LIC-Fusion: LiDAR-Inertial-Camera Odometry](https://mlsanity.com/paper/1909.04102/) (2019)
- [LiDAR-Camera Calibration using 3D-3D Point correspondences](https://mlsanity.com/paper/1705.09785/) (2017)
- [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2202.13708/) (2022)
- [L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras](https://mlsanity.com/paper/2207.01009/) (2022)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras](https://mlsanity.com/paper/2401.08474/) (2024)
- [RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking](https://mlsanity.com/paper/2006.05011/) (2020)
- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [Target-free Extrinsic Calibration of Event-LiDAR Dyad using Edge Correspondences](https://mlsanity.com/paper/2305.04017/) (2023)
- [Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving](https://mlsanity.com/paper/2403.04112/) (2024)
- [A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection](https://mlsanity.com/paper/2504.18419/) (2025)
- [FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2309.11804/) (2023)
- [Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction](https://mlsanity.com/paper/2505.02539/) (2025)

## Built on by (most cited first, 1 of 1)

- [Simplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems](https://mlsanity.com/paper/2608.22965/) (2026)

## Abstract (arXiv)

We present a novel multi-modal extrinsic calibration framework designed to simultaneously estimate the relative poses between event cameras, LiDARs, and RGB cameras, with particular focus on the challenging event camera calibration. Core of our approach is a novel 3D calibration target, specifically designed and constructed to be concurrently perceived by all three sensing modalities. The target encodes features in planes, ChArUco, and active LED patterns, each tailored to the unique characteristics of LiDARs, RGB cameras, and event cameras respectively. This unique design enables a one-shot, joint extrinsic calibration process, in contrast to existing approaches that typically rely on separate, pairwise calibrations. Our calibration pipeline is designed to accurately calibrate complex vision systems in the context of autonomous driving, where precise multi-sensor alignment is critical. We validate our approach through an extensive experimental evaluation on a custom built dataset, recorded with an advanced autonomous driving sensor setup, confirming the accuracy and robustness of our method.