Hu, BingyangGuo, JieChen, YanjunLi, MengtianGuo, YanwenPanozzo, Daniele and Assarsson, Ulf2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13920https://diglib.eg.org:443/handle/10.1111/cgf13920Effective compression of densely sampled BRDF measurements is critical for many graphical or vision applications. In this paper, we present DeepBRDF, a deep-learning-based representation that can significantly reduce the dimensionality of measured BRDFs while enjoying high quality of recovery. We consider each measured BRDF as a sequence of image slices and design a deep autoencoder with a masked L2 loss to discover a nonlinear low-dimensional latent space of the high-dimensional input data. Thorough experiments verify that the proposed method clearly outperforms PCA-based strategies in BRDF data compression and is more robust. We demonstrate the effectiveness of DeepBRDF with two applications. For BRDF editing, we can easily create a new BRDF by navigating on the low-dimensional manifold of DeepBRDF, guaranteeing smooth transitions and high physical plausibility. For BRDF recovery, we design another deep neural network to automatically generate the full BRDF data from a single input image. Aided by our DeepBRDF learned from real-world materials, a wide range of reflectance behaviors can be recovered with high accuracy.Attribution 4.0 International LicenseComputing methodologiesReflectance modelingNeural networks10.1111/cgf.13920157-166