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Poster

DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes

Xiaoyu Zhou · Zhiwei Lin · Xiaojun Shan · Yongtao Wang · Deqing Sun · Ming-Hsuan Yang

Arch 4A-E Poster #201
[ ] [ Paper PDF ]
[ Slides [ Poster
Fri 21 Jun 10:30 a.m. PDT — noon PDT

Abstract:

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.

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