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Deep Reflectance Volumes: Relightable Reconstructions from Multi-view Photometric Images

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12348))

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Abstract

We present a deep learning approach to reconstruct scene appearance from unstructured images captured under collocated point lighting. At the heart of Deep Reflectance Volumes is a novel volumetric scene representation consisting of opacity, surface normal and reflectance voxel grids. We present a novel physically-based differentiable volume ray marching framework to render these scene volumes under arbitrary viewpoint and lighting. This allows us to optimize the scene volumes to minimize the error between their rendered images and the captured images. Our method is able to reconstruct real scenes with challenging non-Lambertian reflectance and complex geometry with occlusions and shadowing. Moreover, it accurately generalizes to novel viewpoints and lighting, including non-collocated lighting, rendering photorealistic images that are significantly better than state-of-the-art mesh-based methods. We also show that our learned reflectance volumes are editable, allowing for modifying the materials of the captured scenes.

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Acknowledgements

We thank Giljoo Nam for help with the comparisons. This work was supported in part by ONR grants N000141712687, N000141912293, N000142012529, NSF grant 1617234, Adobe, the Ronald L. Graham Chair and the UC San Diego Center for Visual Computing.

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Authors and Affiliations

  1. University of California, San Diego, USA

    Sai Bi, Zexiang Xu, David Kriegman & Ravi Ramamoorthi

  2. Adobe Research, San Jose, USA

    Zexiang Xu, Kalyan Sunkavalli, Miloš Hašan & Yannick Hold-Geoffroy

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  1. Sai Bi
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  2. Zexiang Xu
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  3. Kalyan Sunkavalli
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  4. Miloš Hašan
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  7. Ravi Ramamoorthi
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Correspondence to Sai Bi .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Bi, S. et al. (2020). Deep Reflectance Volumes: Relightable Reconstructions from Multi-view Photometric Images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham. https://doi.org/10.1007/978-3-030-58580-8_18

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