用旋转偏心块振动摄像头，持续生成事件信号。
VibES: Induced Vibration for Persistent Event-Based Sensing
- 用旋转偏心质量块制造周期性振动，持续激发事件相机
- 可恢复运动参数，提升图像重建与边缘检测效果
- 无需复杂硬件，适合嵌入式视觉系统
事件相机是一种仿生传感器，异步记录像素级亮度变化。在光照恒定、场景静态或低运动情况下，固定安装的事件相机无法产生任何事件，难以用于多数计算机视觉任务。为解决此问题，现有方法多依赖复杂的硬件或额外光学组件来诱发事件。本文提出一种轻量级方案：通过简单旋转偏心质量块，引入周期性振动以持续激发事件信号。结合运动补偿处理流程，可去除注入的振动，输出无运动畸变的清晰事件流，适用于下游感知任务。我们搭建了硬件原型，并在真实数据集上验证该方法。结果表明，该方法能可靠恢复运动参数，在图像重建与边缘检测性能上均优于无运动诱导的事件感知方式。
原文摘要 · Abstract (English)
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.
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