DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
By Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, Ming-Hsuan Yang. Computer Vision and Pattern Recognition, 2023
472 citations recorded by the source corpus.
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.
Subfields: Automotive Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/95d3b6d595e0510edb535a5071c48240d4bb950a
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In Computer Graphics and Computer-Aided Design
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Citations in the corpus
- Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians (2024, background): “Recently, 3D Gaussian splatting (3D-GS) [5] has pushed the field forward by using anisotropic Gaussian primitives, achieving near-perfect visual quality with efficient training times and tile-based splatting techniques for real-time rendering.”