@inproceedings{
qu2024discgs,
title={DisC-{GS}: Discontinuity-aware Gaussian Splatting},
author={Haoxuan Qu and Zhuoling Li and Hossein Rahmani and Yujun Cai and Jun Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ScbmEmtsH5}
}
Recently, Gaussian Splatting, a method that represents a 3D scene as a collection of Gaussian distributions, has gained significant attention in addressing the task of novel view synthesis. In this paper, we highlight a fundamental limitation of Gaussian Splatting: its inability to accurately render discontinuities and boundaries in images due to the continuous nature of Gaussian distributions. To address this issue, we propose a novel framework enabling Gaussian Splatting to perform discontinuity-aware image rendering. Additionally, we introduce a B\'ezier-boundary gradient approximation strategy within our framework to keep the ``differentiability'' of the proposed discontinuity-aware rendering process. Extensive experiments demonstrate the efficacy of our framework.
DisC-GS: Discontinuity-aware Gaussian Splatting
Haoxuan Qu Zhuoling Li Hossein Rahmani Yujun Cai Jun Liu