Material acquisition using deep learning
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Material acquisition using deep learning

SIGGRAPH Asia 2019 Doctoral Consortium, Number 3, page 1-4 - nov 2019
Download the publication : doctoralConsortium_Deschaintre.pdf [6.7Mo]   poster_sigAsia.pdf [2.1Mo]  
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in pictures. Designing algorithms able to leverage these cues to recover spatiallyvarying bi-directional reflectance distribution functions (SVBRDFs) from a few images has challenged computer graphics researchers for decades. I explore the use of deep learning to tackle lightweight appearance capture and make sense of these visual cues. Our networks are capable of recovering per-pixel normals, diffuse albedo, specular albedo and specular roughness from as little as one picture of a flat surface lit by a hand-held flash. We propose a method which improves its prediction with the number of input pictures, and reaches high quality reconstructions with up to 10 images – a sweet spot between existing single-image and complex multi-image approaches. We introduce several innovations on training data acquisition and network design, bringing clear improvement over the state of the art for lightweight material capture.

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BibTex references

  author       = "Deschaintre, Valentin",
  title        = "Material acquisition using deep learning",
  booktitle    = "SIGGRAPH Asia 2019 Doctoral Consortium",
  number       = "3",
  pages        = "1-4",
  month        = "nov",
  year         = "2019",
  publisher    = "ACM",
  organization = "ACM",
  url          = ""

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» Valentin Deschaintre