  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 dynamic
driving scene reconstruction and enables photorealistic surround-view synthesis
with high-fidelity and multi-camera consistency. Our project page is at:
https://github.com/VDIGPKU/DrivingGaussian.
