Event-based Depth from Focus

Abstract

Depth from focus (DFF) is a well-established method for measuring depth in vision systems. However, its efficacy and accuracy are limited by the slow speed required to capture high-quality focal stacks. We address this limitation by leveraging emerging hardware technologies: the event camera and liquid lens. In this paper, we introduce an innovative approach called Event-Based Depth from Focus (EDFF). We present a prototype system and propose Event Cancellation Score (ECS) as a novel metric to efficiently detect event data focus. To validate the effectiveness of our system, we have curated the first EDFF dataset, which comprises event recordings of focal sweeps performed on 3D-printed test targets. Comparative analysis against existing event focus detection algorithms demonstrates the superior performance of our algorithm in the EDFF task.