Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation

• URL: http://arxiv.org/abs/2509.15224v1
• Date: Thu, 18 Sep 2025 17:59:51 GMT
• Title: Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation
• Authors: Luca Bartolomei, Enrico Mannocci, Fabio Tosi, Matteo Poggi, Stefano Mattoccia,
• Abstract summary: Event cameras capture sparse, high-temporal-resolution visual information.<n>The lack of large datasets with dense ground-truth depth annotations hinders learning-based monocular depth estimation from event data.<n>We propose a cross-modal distillation paradigm to generate dense proxy labels leveraging a Vision Foundation Model (VFM)<n>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.
• Score: 47.90167568304715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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