Capturing Anisotropic SVBRDFs

Julian Kaltheuner, Lukas Bode, and Reinhard Klein
In: Vision, Modeling, and Visualization (VMV) (Sept. 2021)
 

Abstract

In this work, we adapt and improve recent isotropic material estimation efforts to estimate spatially varying anisotropic materials with an additional Fresnel term using a variable set of input images and are able to handle any resolution. We combine an initial estimation network with an auto-encoder to fine-tune the decoding of latent embedded appearance parameters on the input images to produce finely detailed SVBRDFs. For this purpose, the training must be adapted so that the determination is possible on the basis of a small number of images that still capture as much reflective behavior of materials as possible. The resulting appearance parameters are capable of capturing and reconstructing complex spatially varying features in detail, but place increased demands on the input images.

Images

Bibtex

@ARTICLE{kaltheuner2021capturing,
    author = {Kaltheuner, Julian and Bode, Lukas and Klein, Reinhard},
     title = {Capturing Anisotropic SVBRDFs},
   journal = {Vision, Modeling, and Visualization (VMV)},
      year = {2021},
     month = sep,
  abstract = {In this work, we adapt and improve recent isotropic material estimation efforts to estimate
              spatially varying anisotropic materials with an additional Fresnel term using a variable set of
              input images and are able to handle any resolution. We combine an initial estimation network with an
              auto-encoder to fine-tune the decoding of latent embedded appearance parameters on the input images
              to produce finely detailed SVBRDFs. For this purpose, the training must be adapted so that the
              determination is possible on the basis of a small number of images that still capture as much
              reflective behavior of materials as possible. The resulting appearance parameters are capable of
              capturing and reconstructing complex spatially varying features in detail, but place increased
              demands on the input images.}
}