DeepBRDF: A Deep Representation for Manipulating Measured BRDF
Bingyang Hu, Jie Guo, Yanjun Chen, Mengtian Li, Yanwen Guo
Abstract
Abstract Effective 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 L 2 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.