LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization
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 mod...
| Authors | Kuangyi Chen, Jun Zhang, Yuxi Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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