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ARXIV:2603.09385 · 3D PERCEPTION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.093853D PERCEPTIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation.
Opportunity summary
Pain EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation. However, progress is severely hindered by the scarcity of dense depth annotations.
Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in challenging conditions. However, progress is severely hindered by the scarcity…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We achieve this via a comprehensive tri-level distillation strategy: (i) Cross-Modal Feature Mixture (CMFM) bridges the modality gap at the output level by fusing…
3D Perception moved forward this cycle; last verified April 2026. Public score 7.0/10.
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EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation.
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10.48550/arXiv.2603.09385EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation.
Abstract
Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in challenging conditions. However, progress is severely hindered by the scarcity of dense depth annotations. While recent annotation-free approaches mitigate this by distilling knowledge from Vision Foundation Models (VFMs), a critical limitation persists: they process event streams as independent frames. By neglecting the inherent temporal continuity of event data, these methods fail to leverage the rich temporal priors encoded in VFMs, ultimately yielding temporally inconsistent and less accurate depth predictions. To address this, we introduce EventVGGT, a novel framework that explicitly models the event stream as a coherent video sequence. To the best of our knowledge, we are the first to distill spatio-temporal and multi-view geometric priors from the Visual Geometry Grounded Transformer (VGGT) into the event domain. We achieve this via a comprehensive tri-level distillation strategy: (i) Cross-Modal Feature Mixture (CMFM) bridges the modality gap at the output level by fusing RGB and event features to generate auxiliary depth predictions; (ii) Spatio-Temporal Feature Distillation (STFD) distills VGGT's powerful spatio-temporal representations at the feature level; and (iii) Temporal Consistency Distillation (TCD) enforces cross-frame coherence at the temporal level by aligning inter-frame depth changes. Extensive experiments demonstrate that EventVGGT consistently outperforms existing methods -- reducing the absolute mean depth error at 30m by over 53\% on EventScape (from 2.30 to 1.06) -- while exhibiting robust zero-shot generalization on the unseen DENSE and MVSEC datasets.
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Dimensions overall score 7.0
PROBLEM
EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation. However, progress is severely hindered by the scarcity of dense depth annotations.
METHOD
Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in challenging conditions. However, progress is severely hindered by the scarcity of dense depth annota...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We achieve this via a comprehensive tri-level distillation strategy: (i) Cross-Modal Feature Mixture (CMFM) bridges the modality gap at the output level by fusing RGB and event features to generate auxili...
WHY NOW
3D Perception moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation. However, progress is severely hindered by the scarcity of dense depth annotations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in challenging conditions. However, progress is severely hindered by the scarcity of dense depth annotations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We achieve this via a comprehensive tri-level distillation strategy: (i) Cross-Modal Feature Mixture (CMFM) bridges the modality gap at the output level by fusing RGB and event features to generate auxiliary depth predictions; (ii) Spatio-Temporal Feature Distillation (STFD) distills VGGT's powerful spatio-temporal representations at the feature level; and (iii) Temporal Consistency Distillation (TCD) enforces cross-frame coherence at the temporal level by aligning inter-frame depth changes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
3D Perception moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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EventVGGT leverages spatio-temporal knowledge distillation for accurate event-based depth estimation.
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3D Perception
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