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.
@article{arxiv.2405.15196,
title = {DisC-GS: Discontinuity-aware Gaussian Splatting},
author = {Haoxuan Qu and Zhuoling Li and Hossein Rahmani and Yujun Cai and Jun Liu},
journal= {arXiv preprint arXiv:2405.15196},
year = {2024}
}