Per-Pixel Solution of Multispectral Photometric Stereo

Shin Ishihara, Imari Sato; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9148-9157

Photometric Stereo (PS) estimates surface normals by analyzing images lit from different angles. Enhancing PS with spectral imaging known as multispectral photometric stereo (MPS) uses varying light source colors for simultaneous image capture. As in traditional PS obtaining a unique solution is challenging in MPS when the reflectance properties of the object are unknown. This paper presents an approach utilizing the spatial arrangement and color of light sources to solve the MPS problem in the condition of spatially varying reflectance from a minimum of seven spectral images without spatial smoothness constraints. A robust optimization technique is introduced to manage real data. Experiments on synthetic and real scenes validate the method's effectiveness including for non-Lambertian surfaces. The method can contribute to advanced digital archiving that simultaneously records surface normal and spectral reflectance.