NeRD: Neural Reflectance Decomposition from Image Collections

Mark Boss¹, Raphael Braun¹, Varun Jampani², Jonathan T. Barron², Ce Liu² and Hendrik P. A. Lensch¹
University of Tübingen¹ , Google²
ICCV 2021

Decomposing a scene into its shape, reflectance, and illumination is a challenging but essential problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. By decomposing a scene into explicit representations, any rendering framework can be leveraged to generate novel views under any illumination in real-time. NeRD is a method that achieves this decomposition by introducing physically-based rendering to neural radiance fields. Even challenging non-Lambertian reflectances, complex geometry, and unknown illumination can be decomposed to high-quality models.

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@inproceedings{boss2021nerd,
  title={NeRD: Neural Reflectance Decomposition from Image Collections},
  author={Boss, Mark and Braun, Raphael and Jampani, Varun and Barron, Jonathan T. and Liu, Ce and Lensch, Hendrik P.A.},
  year={2021},
  booktitle = {IEEE International Conference on Computer Vision (ICCV)}
}