SurfaceNet: Adversarial SVBRDF Estimation From a Single Image

Giuseppe Vecchio, Simone Palazzo, Concetto Spampinato; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 12840-12848

Abstract


In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) that is able to produce high-quality, high-resolution surface reflectance maps. The employment of the GAN paradigm has a twofold objective: 1) allowing the model to recover finer details than standard translation models; 2) reducing the domain shift between synthetic and real data distributions in an unsupervised way. An extensive evaluation, carried out on a public benchmark of synthetic and real images under different illumination conditions, shows that SurfaceNet largely outperforms existing SVBRDF reconstruction methods, both quantitatively and qualitatively. Furthermore, SurfaceNet exhibits a re-markable ability in generating high-quality maps from real samples without any supervision at training time.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Vecchio_2021_ICCV, author = {Vecchio, Giuseppe and Palazzo, Simone and Spampinato, Concetto}, title = {SurfaceNet: Adversarial SVBRDF Estimation From a Single Image}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {12840-12848} }