Deep scene-scale material estimation from multi-view indoor captures
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Deep scene-scale material estimation from multi-view indoor captures

Computers & Graphics, Volume 109, page 15-29 - October 2022
Download the publication : deep_materials.pdf [26.8Mo]   deep_materials_supp.pdf [4.1Mo]  
The movie and video game industries have adopted photogrammetry as a way to create digital 3D assets from multiple photographs of a real-world scene. But photogrammetry algorithms typically output an RGB texture atlas of the scene that only serves as visual guidance for skilled artists to create material maps suitable for physically-based rendering. We present a learning-based approach that automatically produces digital assets ready for physically-based rendering, by estimating approximate material maps from multi-view captures of indoor scenes that are used with retopologized geometry. We base our approach on a material estimation Convolutional Neural Network (CNN) that we execute on each input image. We leverage the view-dependent visual cues provided by the multiple observations of the scene by gathering, for each pixel of a given image, the color of the corresponding point in other images. This image-space CNN provides us with an ensemble of predictions, which we merge in texture space as the last step of our approach. Our results demonstrate that the recovered assets can be directly used for physically-based rendering and editing of real indoor scenes from any viewpoint and novel lighting. Our method generates approximate material maps in a fraction of time compared to the closest previous solutions.

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Acknowledgements and Funding

The research was funded by the ERC Advanced grant FUNGRAPH No 788065 ( The authors are grateful to Inria Sophia Antipolis-Méditerranée "Nef" computation cluster and the OPAL infrastructure from Université Côte d'Azur for providing resources and support. We thank the associate editor and the anonymous reviewers for their insightful comments which helped improve the manuscript. We also thank B. Bitterli for the scenes in his rendering repository. The authors also extend their thanks to Felix Hähnlein and Emilie Yu for their helpful discussion and support for the project and especially thank the 3D artist Stefania Kousoula for mesh refinement and re-topology.

BibTex references

  author       = "Prakash, Siddhant and Rainer, Gilles and Bousseau, Adrien and Drettakis, George",
  title        = "Deep scene-scale material estimation from multi-view indoor captures",
  journal      = "Computers \& Graphics",
  volume       = "109",
  pages        = "15-29",
  month        = "October",
  year         = "2022",
  url          = ""

Other publications in the database

» Siddhant Prakash
» Gilles Rainer
» Adrien Bousseau
» George Drettakis