Perceptually Motivated BRDF Comparison using Single Image
Surface reflectance of real-world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its evaluation we need a distance function between a reference surface reflectance and its approximate version. Although some of the past techniques tried to reflect the perceptual sensitivity of human vision, they have neither optimized illumination and viewing conditions nor surface shape. In this paper, we suggest a new image-based methodology for comparing different anisotropic BRDFs. We use optimization techniques to generate a novel surface which has extensive coverage of incoming and outgoing light directions, while preserving its features and frequencies that are important for material appearance judgments. A single rendered image of such a surface along with simultaneously optimized lighting and viewing directions leads to the computation of a meaningful BRDF difference, by means of standard image difference predictors. A psychophysical experiments revealed that our surface provides richer information on material properties than the standard surfaces often used in computer graphics, e.g., sphere or blob.
Computer Graphics Forum 35(3), pages 1-12, 2016 (Proceedings of Eurographics Symposium on Rendering 2016) Article [PDF], 12 pages [Supplementary PDF] 29 pages [BibTeX] [Video MP4 - 28 MBytes] [C++ implementation] [Data for Matlab etc.] [Code for Matlab - rendering surfaces] [C++ code fragment - for generating surfaces S2 and S3]
Geometry of surfacesFor those needing the geometry the surfaces in Wavefront OBJ format are here: [Wavefront OBJ of surface1] [Wavefront OBJ of surface2]
It is necessary to set the camera (location, direction, up vector, field of view) towards the surface according to the data in the paper to get the correct images with properties.