DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency

Heng Guo, Jieji Ren, Feishi Wang, Boxin Shi, Mingjun Ren, Yasuyuki Matsushita; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 11810-11820

Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Translucency. Unlike qualitative and semantic labels such as diffuse and specular that have been used in previous datasets our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://photometricstereo.github.io/diligentrt.html.