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Graph."],"published-print":{"date-parts":[[2023,12,5]]},"abstract":"<jats:p>We present the first large-scale database of measured spatially-varying anisotropic reflectance, consisting of 1,000 high-quality near-planar SVBRDFs, spanning 9 material categories such as wood, fabric and metal. Each sample is captured in 15 minutes, and represented as a set of high-resolution texture maps that correspond to spatially-varying BRDF parameters and local frames. To build this database, we develop a novel integrated system for robust, high-quality and -efficiency reflectance acquisition and reconstruction. Our setup consists of 2 cameras and 16,384 LEDs. We train 64 lighting patterns for efficient acquisition, in conjunction with a network that predicts per-point reflectance in a neural representation from carefully aligned two-view measurements captured under the patterns. The intermediate results are further fine-tuned with respect to the photographs acquired under 63 effective linear lights, and finally fitted to a BRDF model. We report various statistics of the database, and demonstrate its value in the applications of material generation, classification as well as sampling. 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