# MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration

arXiv: https://arxiv.org/abs/2405.18021 · HTML: https://mlsanity.com/paper/2405.18021/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 12 of 12)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Separable Self-attention for Mobile Vision Transformers](https://mlsanity.com/paper/2206.02680/) (2022)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow](https://mlsanity.com/paper/2203.09385/) (2022)
- [L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras](https://mlsanity.com/paper/2207.01009/) (2022)
- [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)
- [UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation](https://mlsanity.com/paper/2304.09715/) (2023)
- [PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration](https://mlsanity.com/paper/2309.09855/) (2023)

## Built on by (most cited first, 7 of 7)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach](https://mlsanity.com/paper/2501.06878/) (2025)
- [DELTA: Dense Depth from Events and LiDAR using Transformer's Attention](https://mlsanity.com/paper/2505.02593/) (2025)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)
- [LiREC-Net: A Target-Free and Learning-Based Network for LiDAR, RGB, and Event Calibration](https://mlsanity.com/paper/2602.21754/) (2026)
- [Mind the Hitch: Dynamic Calibration and Articulated Perception for Autonomous Trucks](https://mlsanity.com/paper/2603.23711/) (2026)
- [Simplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems](https://mlsanity.com/paper/2608.22965/) (2026)

## Abstract (arXiv)

Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing comprehensive environmental information - remains unexplored. We introduce MULi-Ev, the first online, deep learning-based framework tailored for the extrinsic calibration of event cameras with LiDAR. This advancement is instrumental for the seamless integration of LiDAR and event cameras, enabling dynamic, real-time calibration adjustments that are essential for maintaining optimal sensor alignment amidst varying operational conditions. Rigorously evaluated against the real-world scenarios presented in the DSEC dataset, MULi-Ev not only achieves substantial improvements in calibration accuracy but also sets a new standard for integrating LiDAR with event cameras in mobile platforms. Our findings reveal the potential of MULi-Ev to bolster the safety, reliability, and overall performance of event-based perception systems in autonomous driving, marking a significant step forward in their real-world deployment and effectiveness.