{'fraction_non_alphanumeric': 0.021701388888888888, 'fraction_numerical': 0.003472222222222222, 'mean_word_length': 5.300546448087432, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.