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 Bézier-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.
最近，高斯喷溅法，一种将三维场景表示为高斯分布集合的方法，在解决新视角合成任务中获得了显著关注。在本文中，我们指出了高斯喷溅的一个基本局限性：由于高斯分布的连续性质，其无法准确渲染图像中的不连续性和边界。为了解决这个问题，我们提出了一个新框架，使高斯喷溅能够进行感知不连续性的图像渲染。此外，我们在框架中引入了一个贝塞尔边界梯度近似策略，以保持所提出的感知不连续性渲染过程的“可微分性”。广泛的实验表明我们框架的有效性。