Title: Visual Timing For Sound Source Depth Estimation in the Wild. Abstract: Depth estimation enables a wide variety of 3D applications, such as robotics and autonomous driving. Despite significant work on various depth sensors, it is challenging to develop an all-in-one method to meet multiple basic criteria. In this paper, we propose a novel audio-visual learning scheme by integrating semantic features with physical spatial cues to boost monocular depth with only one microphone. Inspired by the flash-to-bang theory, we develop FBDepth, the first passive audio-visual depth estimation framework. It is based on the difference between the time-of-flight (ToF) of the light and the sound. We formulate sound source depth estimation as an audio-visual event localization task for collision events. To approach decimeter-level depth accuracy, we design a coarse-to-fine pipeline to push the temporary localization accuracy from event-level to millisecond-level by aligning audio-visual correspondence and manipulating optical flow. FBDepth feeds the estimated visual timestamp together with the audio clip and object visual features to regress the source depth. We use a mobile phone to collect 3.6K+ video clips with 24 different objects at up to 60m. FBDepth shows superior performance especially at a long range compared to monocular and stereo methods.