  Three-dimensional (3D) reconstruction and scene depth estimation from
2-dimensional (2D) images are major tasks in computer vision. However, using
conventional 3D reconstruction techniques gets challenging in participating
media such as murky water, fog, or smoke. We have developed a method that uses
a time-of-flight (ToF) camera to estimate an object region and depth in
participating media simultaneously. The scattering component is saturated, so
it does not depend on the scene depth, and received signals bouncing off
distant points are negligible due to light attenuation in the participating
media, so the observation of such a point contains only a scattering component.
These phenomena enable us to estimate the scattering component in an object
region from a background that only contains the scattering component. The
problem is formulated as robust estimation where the object region is regarded
as outliers, and it enables the simultaneous estimation of an object region and
depth on the basis of an iteratively reweighted least squares (IRLS)
optimization scheme. We demonstrate the effectiveness of the proposed method
using captured images from a Kinect v2 in real foggy scenes and evaluate the
applicability with synthesized data.
