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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)


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.


View synthesis Relighting Appearance acquisition Neural rendering 



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.

Supplementary material

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Supplementary material 1 (pdf 3866 KB)

Supplementary material 2 (mp4 67788 KB)


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CaliforniaSan DiegoUSA
  2. 2.Adobe ResearchSan JoseUSA

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