Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting excels in real-time rendering and static scene reconstructions but struggles with modeling driving scenarios due to complex backgrounds, dynamic objects, and sparse views. We propose AutoSplat, a framework employing Gaussian splatting to achieve highly realistic reconstructions of autonomous driving scenes. By imposing geometric constraints on Gaussians representing the road and sky regions, our method enables multi-view consistent simulation of challenging scenarios including lane changes. Leveraging 3D templates, we introduce a reflected Gaussian consistency constraint to supervise both the visible and unseen side of foreground objects. Moreover, to model the dynamic appearance of foreground objects, we estimate residual spherical harmonics for each foreground Gaussian. Extensive experiments on Pandaset and KITTI demonstrate that AutoSplat outperforms state-of-the-art methods in scene reconstruction and novel view synthesis across diverse driving scenarios.
自动驾驶系统的发展需要逼真的场景重建和视角合成，以模拟安全关键的驾驶场景。3D高斯斑点化在实时渲染和静态场景重建方面表现出色，但在建模驾驶场景时面临复杂背景、动态物体和稀疏视角的挑战。我们提出了AutoSplat，这是一个利用高斯斑点化实现高度逼真的自动驾驶场景重建的框架。通过对代表道路和天空区域的高斯函数施加几何约束，我们的方法能够多视角一致地模拟包括车道变换在内的复杂场景。利用3D模板，我们引入了反射高斯一致性约束，监督前景物体可见和不可见侧的重建。此外，为了模拟前景物体的动态外观，我们为每个前景高斯估计残余球面谐波。 在Pandaset和KITTI数据集上的广泛实验表明，AutoSplat在各种驾驶场景中的场景重建和新视角合成方面优于现有方法。