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, becoming unsuitable for most computer vision tasks. To address this limitation, recent work has investigated motion-induced event stimulation that 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 demonstrate our approach with a hardware prototype and evaluate it on real-world captured datasets. Our method reliably recovers motion parameters and improves both image reconstruction and edge detection over event-based sensing without motion induction.
Abstract (translated)
事件相机是一种受生物启发的传感器类，能够异步地测量每个像素的强度变化。在固定照明条件下的静态或低动态场景中，刚性安装的事件相机无法生成任何事件，从而不适合大多数计算机视觉任务。为了解决这一限制，最近的研究探讨了通过复杂硬件或附加光学组件来诱导运动以刺激事件的方法。相比之下，我们提出了一种轻量级方法，即使用一个简单的旋转不平衡质量来持续地产生周期性振动，从而维持持久的事件生成。这种方法结合了一个运动补偿流水线，该流水线可以消除注入的运动，并为下游感知任务提供干净、经过修正的事件数据。 我们在硬件原型中演示了这一方法，并在实际捕获的数据集上对其进行了评估。我们的方法能够可靠地恢复运动参数，在没有引入运动的情况下改善基于事件感应的图像重建和边缘检测性能。
URL
https://arxiv.org/abs/2508.19094
