GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving

Huasong Han, Kaixuan Zhou, Xiao-Xiao Long, Yusen Wang, Chunxia Xiao

Abstract:

We propose GGS, a Generalizable Gaussian Splatting method for Autonomous Driving that 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 difference 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 introduce a novel virtual lane generation module into GSS method
to enable high-quality lane switching even without a multi-lane dataset. Besides, we design a diffusion loss to supervise the generation of virtual lane images to further address the problem of data lacking 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.