Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter
This addresses the problem of degraded images in environments with scattered light for applications in underwater or atmospheric 3D reconstruction, representing an incremental advancement over previous methods.
The paper tackles 3D reconstruction in participating media like murky water or fog by proposing a photometric stereo method that models shape-dependent forward scatter, using lookup tables and sparse matrix approximations to remove scatter, and demonstrates improved reconstruction in experiments with real and synthesized data.
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.