REALM aligns event representations with ViT-based RGB latent spaces via LoRA, enabling zero-shot transfer of frozen RGB decoders like MASt3R to raw event data with SOTA wide-baseline feature matching.
VibES: Induced Vibration for Persistent Event-Based Sensing
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Event cameras are a bio-inspired class of sensors that asynchronously measure per-pixel intensity changes. Under fixed illumination conditions in static or low-motion scenes, rigidly mounted event cameras are unable to generate any events and become unsuitable for most computer vision tasks. To address this limitation, recent work has investigated motion-induced event stimulation, which often requires complex hardware or additional optical components. In contrast, we introduce a lightweight approach to sustain persistent event generation by employing a simple rotating unbalanced mass to induce periodic vibrational motion. This is combined with a motion-compensation pipeline that removes the injected motion and yields clean, motion-corrected events for downstream perception tasks. We develop a hardware prototype to demonstrate our approach and evaluate it on real-world datasets. Our method reliably recovers motion parameters and improves both image reconstruction and edge detection compared to event-based sensing without motion induction.
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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REALM: An RGB- and Event-Aligned Latent Manifold for Cross-Modal Perception
REALM aligns event representations with ViT-based RGB latent spaces via LoRA, enabling zero-shot transfer of frozen RGB decoders like MASt3R to raw event data with SOTA wide-baseline feature matching.