M. Abadi, . Agarwal-a, . Barham-p, E. Brevdo, . Chen-z et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org

M. Aittala, A. T. Lehtinen, and J. , Reflectance modeling by neural texture synthesis, ACM Transactions on Graphics (Proc. SIG-GRAPH), vol.35, issue.2, p.4, 2016.
DOI : 10.1145/2897824.2925917
URL : http://dl.acm.org/ft_gateway.cfm?id=2925917&type=pdf

M. Aittala and . Durand-f, Burst image deblurring using permutation invariant convolutional neural networks, The European Conference on Computer Vision (ECCV, vol.2, p.3, 2018.
DOI : 10.1007/978-3-030-01237-3_45

, ALLEGORITHMIC: Substance share, 2018.

M. Aittala, T. Weyrich, and . Lehtinen-j, Practical SVBRDF capture in the frequency domain

M. Aittala, T. Weyrich, and J. Lehtinen, Two-shot SVBRDF capture for stationary materials, Proc. SIGGRAPH), vol.34, 2015.
DOI : 10.1145/2766967

C. G. Han-k and . K. Wong-k.-y, Ps-fcn: A flexible learning framework for photometric stereo, The European Conference on Computer Vision (ECCV), vol.2, p.3, 2018.

. L. Cook-r and . E. Torrance-k, A reflectance model for computer graphics, ACM Transactions on Graphics, vol.1, issue.3, pp.7-24, 1982.

. B. Choy-c, . Xu-d, J. Gwak, . Chen-k, and . Savarese-s, A unified approach for single and multi-view 3d object reconstruction, IEEE European Conference on Computer Vision (ECCV), pp.628-644, 2016.

. Deschaintre-v, M. Aittala, . Durand-f, G. Drettakis, and . Bousseau-a, Single-image svbrdf capture with a rendering-aware deep network, ACM Transactions on Graphics (SIGGRAPH Conference Proceedings), vol.37, p.13, 2018.

D. Y. Chen-g, . Peers-p, J. Zhang, and . Tong-x, Appearance-from-motion: Recovering spatially varying surface reflectance under unknown lighting, ACM Transactions on Graphics (Proc. SIGGRAPH Asia), vol.33, issue.2, p.6, 2014.

. J. Dana-k, . Van-ginneken-b, . K. Nayar-s, and J. J. Koen-derink, Reflectance and texture of real-world surfaces, ACM Transactions On Graphics (TOG), vol.18, issue.2, pp.1-34, 1999.

D. Y. Wang, J. Tong-x, J. Snyder, . Ben-ezra-m, Y. Lan et al., Manifold bootstrapping for svbrdf capture, ACM Transactions on Graphics (Proc. SIGGRAPH), vol.29, issue.2, p.4, 2010.

. Ghosh-a, . Chen-t, . Peers-p, . A. Wilson-c, and P. Debevec, Our method recovers more normal details, and better removes highlight and shading residuals from the diffuse albedo. See supplemental materials for more comparisons and results, Comparison against single-image methods on real-world pictures, vol.10, 2009.

. Guarnera-d, . C. Guarnera-g, . Ghosh-a, C. Denk, and . Glencross-m, BRDF Representation and Acquisition, 2016.

G. A. Tchou, C. Hawkins-t, and P. Debevec, Linear light source reflectometry, ACM Trans. Graph, vol.22, pp.749-758, 2003.

H. Z. Sunkavalli, K. Lee, J. Y. Hadap, S. Wang, and J. C. Sankaranarayanan-a, Reflectance capture using univariate sampling of brdfs, IEEE International Conference on Computer Vision (ICCV, 2017.

. P. Kingma-d and J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR, vol.3, p.6, 2015.

. Kang-k, . Chen-z, J. Wang, . Zhou-k, and H. Wu, Efficient reflectance capture using an autoencoder, Proc. SIGGRAPH), vol.37, 2018.

. Liu-g, D. Ceylan, E. Yumer, Y. J. , and L. , Material editing using a physically based rendering network, IEEE International Conference on Computer Vision (ICCV, pp.2261-2269, 2017.

L. X. Dong, Y. Peers-p, and . Tong-x, Modeling surface apc 2019 The Author(s)

, Computer Graphics Forum c 2019 The Eurographics Association and

V. Deschaintre, M. Aittala, F. Durand, and G. , Drettakis & A. Bousseau / Flexible SVBRDF Capture with a Multi-Image Deep Network

. Liu-r, J. Lehman, P. Molino, . P. Such-f, . Frank-e et al., An intriguing failing of convolutional neural networks and the coordconv solution, 2018.

. Lombardi-s and . Nishino-k, Reflectance and illumination recovery in the wild, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), pp.129-141, 2016.

L. Z. Sunkavalli, K. Chandraker, and M. , Materials for masses: SVBRDF acquisition with a single mobile phone image. Proceedings of ECCV, 2018.

L. Z. Xu-z, . Ramamoorthi-r, K. Sunkavalli, and . Chan-draker-m, Learning to reconstruct shape and spatially-varying reflectance from a single image, Proc. SIGGRAPH Asia, vol.2, p.4, 2018.

. K. Mcallister-d, A Generalized Surface Appearance Representation for Computer Graphics, vol.1, 2002.

J. A. Paterson and C. D. Fitzgibbon-a, Brdf and geometry capture from extended inhomogeneous samples using flash photography, Computer Graphics Forum (Proc. Eurographics), vol.24, issue.2, pp.383-391, 2005.

C. R. Qi, H. Su, . Mo-k, and L. J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2017.

. Rematas-k, S. Georgoulis, T. Ritschel, E. Gavves, . Fritz-m et al., Reflectance and natural illumination from single-material specular objects using deep learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.4, 2017.

P. Ronneberger-o, . Fischer, and . Brox-t, U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015), vol.9351, pp.234-241

J. Riviere, . Peers-p, and . Ghosh-a, Mobile surface reflectometry, Computer Graphics Forum, vol.35, issue.2, p.1, 2016.
DOI : 10.1111/cgf.12719

J. Riviere, . Reshetouski-i, L. Filipi, and . Ghosh-a, Polarization imaging reflectometry in the wild, Proc. SIGGRAPH, 2017.

P. Ren, J. Wang, J. Snyder, . Tong-x, and . Guo-b, Pocket reflectometry, vol.30, p.4, 2011.
DOI : 10.1145/1964921.1964940

M. Weinmann, J. Gall, and . Klein-r, Material classification based on training data synthesized using a btf database, European Conference on Computer Vision (ECCV), pp.156-171, 2014.

W. Snavely and N. Marschner-s, Estimating dual-scale properties of glossy surfaces from step-edge lighting, ACM Transactions on Graphics (Proc. SIGGRAPH Asia), vol.30, issue.2, p.6, 2011.

. Wiles-o and . Zisserman-a, Silnet : Single-and multi-view reconstruction by learning from silhouettes. British Machine Vision Conference (BMVC, 2017.

Y. W. Li-x, Y. Dong, . Peers-p, and . Tong-x, Single image surface appearance modeling with self-augmented cnns and inexact supervision, Computer Graphics Forum, vol.37, issue.2, pp.201-211, 2018.

M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, . R. Salakhutdinov-r et al., Deep sets, Advances in Neural Information Processing Systems (NIPS, 2017.