Neural Microfacet Fields for Inverse Rendering
Abstract
We present Neural Microfacet Fields, a method for recovering materials, geometry (volumetric density), and environmental illumination from a collection of images of a scene. Our method applies a microfacet reflectance model within a volumetric setting by treating each sample along the ray as a surface, rather than an emitter. Using surface-based Monte Carlo rendering in a volumetric setting enables our method to perform inverse rendering efficiently and enjoy recent advances in volume rendering. Our approach obtains similar performance as state-of-the-art methods for novel view synthesis and outperforms prior work in inverse rendering, capturing high fidelity geometry and high frequency illumination details.
Cite
Text
Mai et al. "Neural Microfacet Fields for Inverse Rendering." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.00044Markdown
[Mai et al. "Neural Microfacet Fields for Inverse Rendering." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/mai2023iccv-neural/) doi:10.1109/ICCV51070.2023.00044BibTeX
@inproceedings{mai2023iccv-neural,
title = {{Neural Microfacet Fields for Inverse Rendering}},
author = {Mai, Alexander and Verbin, Dor and Kuester, Falko and Fridovich-Keil, Sara},
booktitle = {International Conference on Computer Vision},
year = {2023},
pages = {408-418},
doi = {10.1109/ICCV51070.2023.00044},
url = {https://mlanthology.org/iccv/2023/mai2023iccv-neural/}
}