  We propose a learning-based multi-view stereo (MVS) method in scattering
media, such as fog or smoke, with a novel cost volume, called the dehazing cost
volume. Images captured in scattering media are degraded due to light
scattering and attenuation caused by suspended particles. This degradation
depends on scene depth; thus, it is difficult for traditional MVS methods to
evaluate photometric consistency because the depth is unknown before
three-dimensional (3D) reconstruction. The dehazing cost volume can solve this
chicken-and-egg problem of depth estimation and image restoration by computing
the scattering effect using swept planes in the cost volume. We also propose a
method of estimating scattering parameters, such as airlight, and a scattering
coefficient, which are required for our dehazing cost volume. The output depth
of a network with our dehazing cost volume can be regarded as a function of
these parameters; thus, they are geometrically optimized with a sparse 3D point
cloud obtained at a structure-from-motion step. Experimental results on
synthesized hazy images indicate the effectiveness of our dehazing cost volume
against the ordinary cost volume regarding scattering media. We also
demonstrated the applicability of our dehazing cost volume to real foggy
scenes.

Computer Vision and Pattern Recognition