Abstract
In this paper, we explore an open research problem concerning the reconstruction of 3D scenes from images. Recent methods have adopt 3D Gaussian Splatting (3DGS) to produce 3D scenes due to its efficient training process. However, these methodologies may generate incomplete 3D scenes or blurred multiviews. This is because of (1) inaccurate 3DGS point initialization and (2) the tendency of 3DGS to flatten 3D Gaussians with the sparse-view input. To address these issues, we propose a novel framework EG-Gaussian, which utilizes epipolar geometry and graph networks for 3D scene reconstruction. Initially, we integrate epipolar geometry into the 3DGS initialization phase to enhance initial 3DGS point construction. Then, we specifically design a graph learning module to refine 3DGS spatial features, in which we incorporate both spatial coordinates and angular relationships among neighboring points. Experiments on indoor and outdoor benchmark datasets demonstrate that our approach significantly improves reconstruction accuracy compared to 3DGS-based methods.
Abstract (translated)
在这篇论文中，我们探讨了一个关于从图像重建三维场景的开放性研究问题。最近的方法采用了3D高斯点阵（3D Gaussian Splatting, 3DGS），因为它具有高效的训练过程。然而，这些方法可能会生成不完整的三维场景或模糊的多视角图。这主要是由于两个原因：(1) 不准确的3DGS点初始化；(2) 在处理稀疏视图输入时，3DGS倾向于将3D高斯函数扁平化。为了解决这些问题，我们提出了一种新的框架EG-Gaussian，该框架利用了单应几何和图形网络来进行三维场景重建。首先，我们将单应几何集成到3DGS的初始化阶段中以增强初始的3DGS点构建。然后，我们专门设计了一个图学习模块来细化3DGS的空间特征，在此过程中，我们将空间坐标以及相邻点之间的角度关系都考虑在内。在室内和室外基准数据集上的实验表明，与基于3DGS的方法相比，我们的方法显著提高了重建的准确性。
URL
https://arxiv.org/abs/2504.13540
