Subsurface Scattering for Gaussian Splatting
Abstract
3D reconstruction and relighting of objects made from scattering materials present a significant challenge due to the complex light transport beneath the surface. 3D Gaussian Splatting introduced high-quality novel view synthesis at real-time speeds. While 3D Gaussians efficiently approximate an object's surface, they fail to capture the volumetric properties of subsurface scattering. We propose a framework for optimizing an object's shape together with the radiance transfer field given multi-view OLAT (one light at a time) data. Our method decomposes the scene into an explicit surface represented as 3D Gaussians, with a spatially varying BRDF, and an implicit volumetric representation of the scattering component. A learned incident light field accounts for shadowing. We optimize all parameters jointly via ray-traced differentiable rendering. Our approach enables material editing, relighting, and novel view synthesis at interactive rates. We show successful application on synthetic data and contribute a newly acquired multi-view multi-light dataset of objects in a light-stage setup. Compared to previous work we achieve comparable or better results at a fraction of optimization and rendering time while enabling detailed control over material attributes.
Cite
Text
Dihlmann et al. "Subsurface Scattering for Gaussian Splatting." Neural Information Processing Systems, 2024. doi:10.52202/079017-3870Markdown
[Dihlmann et al. "Subsurface Scattering for Gaussian Splatting." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/dihlmann2024neurips-subsurface/) doi:10.52202/079017-3870BibTeX
@inproceedings{dihlmann2024neurips-subsurface,
title = {{Subsurface Scattering for Gaussian Splatting}},
author = {Dihlmann, Jan-Niklas and Majumdar, Arjun and Engelhardt, Andreas and Braun, Raphael and Lensch, Hendrik P.A.},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-3870},
url = {https://mlanthology.org/neurips/2024/dihlmann2024neurips-subsurface/}
}