  Images captured in participating media such as murky water, fog, or smoke are
degraded by scattered light. Thus, the use of traditional three-dimensional
(3D) reconstruction techniques in such environments is difficult. In this
paper, we propose a photometric stereo method for participating media. The
proposed method differs from previous studies with respect to modeling
shape-dependent forward scatter. In the proposed model, forward scatter is
described as an analytical form using lookup tables and is represented by
spatially-variant kernels. We also propose an approximation of a large-scale
dense matrix as a sparse matrix, which enables the removal of forward scatter.
Experiments with real and synthesized data demonstrate that the proposed method
improves 3D reconstruction in participating media.
