Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter

TL;DR

This paper introduces a photometric stereo technique tailored for participating media like fog or water, modeling shape-dependent forward scatter to enhance 3D reconstruction accuracy in degraded environments.

Contribution

It presents a novel model of forward scatter using lookup tables and spatially-variant kernels, enabling effective removal of scatter effects in 3D reconstruction.

Findings

Improved 3D reconstruction quality in participating media

Effective modeling of shape-dependent forward scatter

Demonstrated success with real and synthetic data

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.