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We present DrivingGaussian, an efficient and effective framework forsurrounding dynamic autonomous driving scenes. For complex scenes with movingobjects, we first sequentially and progressively model the static background ofthe entire scene with incremental static 3D Gaussians. We then leverage acomposite dynamic Gaussian graph to handle multiple moving objects,individually reconstructing each object and restoring their accurate positionsand occlusion relationships within the scene. We further use a LiDAR prior forGaussian Splatting to reconstruct scenes with greater details and maintainpanoramic consistency. DrivingGaussian outperforms existing methods in drivingscene reconstruction and enables photorealistic surround-view synthesis withhigh-fidelity and multi-camera consistency. The source code and trained modelswill be released.

DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes