We propose GGS, a Generalizable Gaussian Splatting method for Autonomous Driving which can achieve realistic rendering under large viewpoint changes. Previous generalizable 3D gaussian splatting methods are limited to rendering novel views that are very close to the original pair of images, which cannot handle large differences in viewpoint. Especially in autonomous driving scenarios, images are typically collected from a single lane. The limited training perspective makes rendering images of a different lane very challenging. To further improve the rendering capability of GGS under large viewpoint changes, we introduces a novel virtual lane generation module into GSS method to enables high-quality lane switching even without a multi-lane dataset. Besides, we design a diffusion loss to supervise the generation of virtual lane image to further address the problem of lack of data in the virtual lanes. Finally, we also propose a depth refinement module to optimize depth estimation in the GSS model. Extensive validation of our method, compared to existing approaches, demonstrates state-of-the-art performance.

本研究解决了现有一般化三维高斯溅射方法在处理大视角变化时的渲染限制，尤其是在自动驾驶情境中，车道切换的图像渲染颇具挑战。本文提出了一种新型的虚拟车道生成模块和扩散损失来改善高质量渲染能力，并优化深度估计，验证结果显示其优于现有方法，具有显著的应用潜力。

GGS：通用高斯溅射在自动驾驶中车道切换的应用