Photometric Stereo Super Resolution via Complex Surface Structure Estimation

Abstract

Photometric stereo, which derives per-pixel surface normals from shading cues, faces challenges in capturing high-resolution (HR) images in linear response systems. We address the representation of HR surface normals from low-resolution (LR) photometric stereo images. To represent fine details of the surface normal in the HR domain, we propose a novel plug-in high-frequency representation module named the Complex Surface Structure (CSS) estimator. When combined with a conventional photometric stereo model, CSS is capable of representing intricate surface structures in 2D Fourier space. We show that photometric stereo super-resolution (SR) with our CSS estimator provides high-fidelity surface normal representations in higher resolution from the LR inputs. Experiments demonstrate that our results are quantitatively (MAE is less than half of the recent works) and qualitatively better than those of the existing deep learning-based SR work.

Keywords

• Photometric stereo
• super-resolution
• implicit neural representation