EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation

Event cameras offer superior sensitivity to high-speed motionand extreme lighting, making event-based monocular depth estimation apromising approach for robust 3D perception in challenging conditions.However, progress is severely hindered by the scarcity of dense depthannotations. While recent annotation-free approaches mitigate this bydistilling knowledge from Vision Foundation Models (VFMs), a criticallimitation persists: they process event streams as independent frames. Byneglecting the inherent temporal continuity of event data, these meth-ods fail to leverage the rich temporal priors encoded in VFMs, ultimatelyyielding temporally inconsistent and less accurate depth predictions. Toaddress this, we introduce EventVGGT, a novel framework that ex-plicitly models the event stream as a coherent video sequence. To thebest of our knowledge, we are the first to distill spatio-temporal andmulti-view geometric priors from the Visual Geometry Grounded Trans-former (VGGT) into the event domain. We achieve this via a comprehen-sive tri-level distillation strategy: (i) Cross-Modal Feature Mixture(CMFM) bridges the modality gap at the output level by fusing RGBand event features to generate auxiliary depth predictions; (ii) Spatio-Temporal Feature Distillation (STFD) distills VGGT’s powerfulspatio-temporal representations at the feature level; and (iii) Tempo-ral Consistency Distillation (TCD) enforces cross-frame coherenceat the temporal level by aligning inter-frame depth changes. Extensiveexperiments demonstrate that EventVGGT consistently outperforms ex-isting methods—reducing the absolute mean depth error at 30m by over53% on EventScape (from 2.30 to 1.06)—while exhibiting robust zero-shot generalization on the unseen DENSE and MVSEC datasets. Thecode is available at https://github.com/yinruiRen/EventVGGT.