Event cameras capture sparse, high-temporal-resolution visual information, making them particularly suitable for challenging environments with high-speed motion and strongly varying lighting conditions. However, the lack of large datasets with dense ground-truth depth annotations hinders learning-based monocular depth estimation from event data. To address this limitation, we propose a cross-modal distillation paradigm to generate dense proxy labels leveraging a Vision Foundation Model (VFM). Our strategy requires an event stream spatially aligned with RGB frames, a simple setup even available off-the-shelf, and exploits the robustness of large-scale VFMs. Additionally, we propose to adapt VFMs, either a vanilla one like Depth Anything v2 (DAv2), or deriving from it a novel recurrent architecture to infer depth from monocular event cameras. We evaluate our approach with synthetic and real-world datasets, demonstrating that i) our cross-modal paradigm achieves competitive performance compared to fully supervised methods without requiring expensive depth annotations, and ii) our VFM-based models achieve state-of-the-art performance.

End-to-end autonomous driving systems have recently made rapid progress, thanks to simulators such as CARLA. They can drive without infraction of common driving rules on uncongested roads but are still struggling with dense traffic scenarios. We conjecture that this occurs because it lacks understanding of the dynamics of the surrounding vehicles, caused by the absence of explicit short-term memory within the perception path of end- to-end models. To address this challenge, we revise the perception module to explicitly model temporal information, by extending it with an auxiliary task that is well-known in computer vision research: optical flow. We generate a novel benchmark using the CARLA simulator to train our model, FlowFuser, and prove its superior ability to avoid collisions with other agents on the road.