Radiometry modeling with variable atmospheric degradations using neural radiance fields and general adversarial networks

Neural Radiance Fields (NeRFs) have become a benchmark for 3D modeling. Despite their impressive capabilities, the performance of NeRFs is largely dependent on the quality of the input images. To address this, we propose integrating superresolution techniques with NeRFs to enhance 3D model fidelity. Our approach employs exposure correction to overcome model convergence failures resulting from geometric inconsistencies in Generative Adversarial Network (GAN) outputs. While previous studies have explored geometric consistency using refinement networks and inverse degradation pipelines, our solution seamlessly connects image restoration to the ultimate goal of 3D reconstruction. We report an improvement of 0.1065 in LPIPS across our degradation levels and models.