GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving
This addresses the challenge of limited training perspectives in autonomous driving for rendering different lanes, though it appears incremental as it builds on existing 3D Gaussian splatting methods.
The paper tackles the problem of realistic rendering under large viewpoint changes in autonomous driving, particularly for lane switching, by proposing GGS with a virtual lane generation module and diffusion loss, achieving state-of-the-art performance.
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.