BEV${}^2$3D: BEV-Based Event-Guided 3D Detection for LiDAR Blind Intervals

Modern 3D object detectors for autonomous driving often operate at the rate of their slowest sensor, leaving 50–100,ms LiDAR blind intervals without updated detections. To update detections within these intervals, Ev-3DOD introduced event-based blind-time perception through an RoI-based formulation that updates individual detections. We instead introduce BEV${}^2$3D, a framework named after the two EV's in B**EV** and **EV**ent, which reformulates blind-interval perception as event-guided BEV updating. BEV${}^2$3D couples a frozen LiDAR reference detector with an event-driven branch that updates a sparse bird's-eye-view (BEV) representation and re-estimates detections throughout each LiDAR blind interval. Within this event-driven branch, Frustum Event Lifting (FEL) lifts event features with an inference-time LiDAR depth prior, while Support-Gated Temporal Fusion (SGTF) combines fresh event evidence with cached LiDAR features. On DSEC-3DOD and Ev-Waymo, BEV${}^2$3D outperforms Ev-3DOD in every evaluated category by up to 8.6 and 11.3 AP, respectively, while relying only on LiDAR and events, without the RGB modality used by Ev-3DOD. Our code is included in the supplementary material and will be publicly released upon acceptance.