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.

翻译：我们提出DrivingGaussian，一个高效且有效的框架，用于处理周边动态自动驾驶场景。对于包含运动物体的复杂场景，我们首先利用增量式静态三维高斯分布，逐步序列化地建模整个场景的静态背景。随后，我们采用复合动态高斯图处理多个运动物体，单独重建每个物体，并恢复它们在该场景中的精确位置和遮挡关系。我们进一步利用基于激光雷达先验的高斯泼溅技术，以重建更详细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，并能够实现高保真度和多相机一致性的逼真环视合成。源代码和训练好的模型将公开发布。