Event-Based Visual Vibrometry

Abstract

Visual vibrometry has emerged as a powerful technique for remote acquisition of audio and the physical properties of materials. To capture high-frequency vibrations, frame-based approaches often require a high-speed video camera and bright lighting to compensate for the short exposure time. In this paper, we introduce event-based visual vibrometry, a new high-speed visual vibration sensing method using an event camera. By leveraging the high temporal resolution and low bandwidth characteristics of event cameras, event-based visual vibrometry enables high-speed vibration sensing under ambient lighting conditions with improved data efficiency. Specifically, we leverage a hybrid camera system and propose an event-based subtle motion estimation framework that integrates an optimization-based approach based on the event generation model and a motion refinement network. We demonstrate our method by capturing vibration caused by audio sources and estimating material properties for various objects.