GGS：面向自动驾驶中车道切换的可泛化高斯泼溅方法
GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving
AI总结：
本文提出可泛化高斯泼溅方法GGS，通过虚拟车道生成模块、扩散损失监督和深度细化模块，解决自动驾驶中单车道训练数据导致的大视角车道切换渲染难题，实现了最先进性能。
摘要：
我们提出GGS，一种面向自动驾驶的可泛化高斯泼溅方法，能够在大视角变化下实现逼真渲染。以往的可泛化三维高斯泼溅方法仅限于渲染与原始图像对非常接近的新视角，无法处理视角差异较大的情况。尤其在自动驾驶场景中，图像通常从单一车道采集，有限的训练视角使得渲染不同车道的图像极具挑战性。为了进一步提升GGS在大视角变化下的渲染能力，我们在GGS方法中引入了一个新颖的虚拟车道生成模块，从而即使没有多车道数据集也能实现高质量的车道切换。此外，我们设计了一种扩散损失来监督虚拟车道图像的生成，以进一步解决虚拟车道中数据缺乏的问题。最后，我们还提出了一个深度细化模块来优化GGS模型中的深度估计。与现有方法相比，对我们方法的广泛验证展示了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
