NeRD: Neural Reflectance Decomposition from Image Collections release_3limz6ihizecpai6es43ldorbm

by Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P.A. Lensch

Released as a article .

2021  

Abstract

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 into high-quality models. The datasets and code is available on the project page: https://markboss.me/publication/2021-nerd/
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Type  article
Stage   submitted
Date   2021-05-19
Version   v3
Language   en ?
arXiv  2012.03918v3
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