PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely
4/1/2021
Keywords: Material/Lighting Estimation
Venue: CVPR 2021
Bibtex:
@inproceedings{zhang2021physg,
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
author = {Kai Zhang and Fujun Luan and Qianqian Wang and Kavita Bala and Noah Snavely},
title = {PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting},
year = {2021},
url = {http://arxiv.org/abs/2104.00674v1},
entrytype = {inproceedings},
id = {zhang2021physg}
}
Abstract
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
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