PGNN-based Approach for Robust 3D Light Direction Estimation in Outdoor Images

Estimating the 3D light direction from 2D outdoor images is a crucial task in computer vision, especially useful in the context of image forgery detection. In this paper, we propose a novel approach that leverages a physics-guided neural network (PGNN) to achieve accurate global 3D light direction estimation. The proposed architecture incorporates an illumination model that enables the network to indirectly learn geometric information and improve the accuracy of the estimated light direction. To evaluate the performance of our proposed approach, we train and test our models on two datasets that were curated for the global light direction estimation. The proposed PGNN method demonstrates superior performance compared to existing state-of-the-art approaches, where direct comparisons were feasible. To further investigate the benefits provided by embedding the physical model in our approach, we conducted an extensive ablation study, demonstrating that the use of the illumination model significantly enhances the accuracy of the light direction estimation compared to a purely data-driven approach. The proposed PGNN is available as open-source software, providing an accessible and useful tool for researchers in computer vision and graphics.