让自动驾驶图像在大视角切换时仍能真实渲染。
GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving
- 引入虚拟车道生成模块，无需多车道数据即可实现高质量换道渲染。
- 设计扩散损失，提升虚拟车道图像生成质量，缓解数据不足问题。
- 新增深度优化模块，改善原模型在复杂场景下的深度估计精度。
我们提出GGS，一种适用于自动驾驶的可泛化高斯点渲染方法，可在大幅视角变化下实现逼真图像重建。现有可泛化3D高斯点渲染方法仅能处理与原始图像视角接近的新视角，难以应对自动驾驶中不同车道间的显著视角差异。由于自动驾驶图像通常仅从单一路段采集，训练视角受限，导致跨车道渲染困难。为提升GGS在大视角变化下的渲染能力，我们在GSS方法中引入新颖的虚拟车道生成模块，使模型即使在无多车道数据的情况下也能实现高质量换道渲染。此外，设计了扩散损失以监督虚拟车道图像生成，缓解虚拟车道数据匮乏的问题。最后，提出深度精修模块，优化GSS模型中的深度估计性能。大量实验表明，该方法在各项指标上均达到当前最优水平。
原文摘要 · Abstract (English)
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.
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