Linear Unmixing in BRDF Reproduction and 3D Shape Recovery

Linear unmixing is a widely known and frequently applied technique in remote sensing, with the purpose of identifying the combination of materials that compose the multispectral reflection of a measured pixel. In this paper, we apply the same concept to surface reflectance modeling by estimating a reflectance under arbitrary conditions based on a linear combination of 100 basic BRDFs. These so-called end members are taken from the MERL Database of densely sampled BRDF data of different materials (data available at: http://www.merl.com/brdf/). It is shown that the linear unmixing is an effective method to reproduce real reflection behavior accurately. Furthermore, the linearly unmixed BRDF was utilized as a lookup table for Photometric Stereo based surface reconstruction. The reconstruction accuracy is compared with two exemplarily chosen, commonly used parametric BRDF models, the Lambert+Blinn and the Lambert Cook-Torrance model. The linearly mixed BRDF results in accurately reconstructed surfaces and furthermore proves to be a robust method compared to the analytical models.