# LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization

arXiv: https://arxiv.org/abs/2603.01839 · HTML: https://mlsanity.com/paper/2603.01839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](https://mlsanity.com/paper/1607.03468/) (2016)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [LCD: Learned Cross-Domain Descriptors for 2D-3D Matching](https://mlsanity.com/paper/1911.09326/) (2019)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [A Simple and Effective Point-based Network for Event Camera 6-DOFs Pose Relocalization](https://mlsanity.com/paper/2403.19412/) (2024)
- [L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras](https://mlsanity.com/paper/2207.01009/) (2022)
- [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://mlsanity.com/paper/2311.18433/) (2023)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions](https://mlsanity.com/paper/2401.08043/) (2024)
- [Improving Visual Place Recognition Based Robot Navigation By Verifying Localization Estimates](https://mlsanity.com/paper/2407.08162/) (2024)
- [Target-free Extrinsic Calibration of Event-LiDAR Dyad using Edge Correspondences](https://mlsanity.com/paper/2305.04017/) (2023)
- [GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting](https://mlsanity.com/paper/2409.19228/) (2024)
- [EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization](https://mlsanity.com/paper/2408.01370/) (2024)
- [EVLoc: Event-based Visual Localization in LiDAR Maps via Event-Depth Registration](https://mlsanity.com/paper/2503.00167/) (2025)

## Abstract (arXiv)

Event cameras offer high-temporal-resolution sensing that remains reliable under high-speed motion and challenging lighting, making them promising for localization from LiDAR point clouds in GPS-denied and visually degraded environments. However, aligning sparse, asynchronous events with dense LiDAR maps is fundamentally ill-posed, as direct correspondence estimation suffers from modality gaps. We propose LEAR, a dual-task learning framework that jointly estimates edge structures and dense event-depth flow fields to bridge the sensing-modality divide. Instead of treating edges as a post-hoc aid, LEAR couples them with flow estimation through a cross-modal fusion mechanism that injects modality-invariant geometric cues into the motion representation, and an iterative refinement strategy that enforces mutual consistency between the two tasks over multiple update steps. This synergy produces edge-aware, depth-aligned flow fields that enable more robust and accurate pose recovery via Perspective-n-Point (PnP) solvers. On several popular and challenging datasets, LEAR achieves superior performance over the best prior method. The source code, trained models, and demo videos are made publicly available online.