  Depth estimation enables a wide variety of 3D applications, such as robotics,
autonomous driving, and virtual reality. Despite significant work in this area,
it remains open how to enable accurate, low-cost, high-resolution, and
large-range depth estimation. Inspired by the flash-to-bang phenomenon (i.e.
hearing the thunder after seeing the lightning), this paper develops FBDepth,
the first audio-visual depth estimation framework. It takes the difference
between the time-of-flight (ToF) of the light and the sound to infer the sound
source depth. FBDepth is the first to incorporate video and audio with both
semantic features and spatial hints for range estimation. It first aligns
correspondence between the video track and audio track to locate the target
object and target sound in a coarse granularity. Based on the observation of
moving objects' trajectories, FBDepth proposes to estimate the intersection of
optical flow before and after the sound production to locate video events in
time. FBDepth feeds the estimated timestamp of the video event and the audio
clip for the final depth estimation. We use a mobile phone to collect 3000+
video clips with 20 different objects at up to $50m$. FBDepth decreases the
Absolute Relative error (AbsRel) by 55\% compared to RGB-based methods.
