We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. The source code and trained models will be released.
我们提出了DrivingGaussian，这是一个高效且有效的框架，用于处理环绕动态自动驾驶场景。对于具有移动物体的复杂场景，我们首先使用增量静态3D高斯，顺序且逐步地模拟整个场景的静态背景。然后，我们利用复合动态高斯图来处理多个移动物体，分别重建每个物体，并恢复它们在场景中的准确位置和遮挡关系。我们进一步使用激光雷达先验进行高斯喷溅，以重建更多细节的场景，并保持全景一致性。DrivingGaussian在驾驶场景重建方面优于现有方法，并能实现高保真度和多摄像机一致性的逼真环绕视图合成。源代码和训练好的模型将会发布。