3D Gaussian Splatting (3DGS) integrates the strengths of primitive-based representations and volumetric rendering techniques, enabling real-time, high-quality rendering. However, 3DGS models typically overfit to single-scene training and are highly sensitive to the initialization of Gaussian ellipsoids, heuristically derived from Structure from Motion (SfM) point clouds, which limits both generalization and practicality. To address these limitations, we propose GS-Net, a generalizable, plug-and-play 3DGS module that densifies Gaussian ellipsoids from sparse SfM point clouds, enhancing geometric structure representation. To the best of our knowledge, GS-Net is the first plug-and-play 3DGS module with cross-scene generalization capabilities. Additionally, we introduce the CARLA-NVS dataset, which incorporates additional camera viewpoints to thoroughly evaluate reconstruction and rendering quality. Extensive experiments demonstrate that applying GS-Net to 3DGS yields a PSNR improvement of 2.08 dB for conventional viewpoints and 1.86 dB for novel viewpoints, confirming the method's effectiveness and robustness.

本研究旨在解决3D高斯喷溅模型在单场景训练中过拟合与高斯椭球体初始化敏感性的问题，提出了GS-Net，即一种通用的即插即用3D高斯喷溅模块，通过从稀疏的SfM点云中增强高斯椭球体的密度。研究结果表明，GS-Net在传统和新颖视点上显著提高了重建和渲染质量，展示了其广泛的实用性和强大的适应能力。

GS-Net：通用即插即用3D高斯喷溅模块