Photometric Stereo in Participating Media Considering Shape-Dependent
Forward Scatter
Abstract
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