Robust 4D Driving Scene Reconstruction from Imperfect Visual Priors
Reconstructing 4D driving scenes in the wild (e.g., internet and AI-generated videos) is critical for diverse autonomous driving simulation. While recent Gaussian Scene Graph (GSG) methods achieve impressive visual quality, they heavily rely on precise priors, such as accurate camera poses and LiDAR depth, or manual annotations. When initialized with noisy priors estimated from in-the-wild videos, existing GSG methods suffer from optimization ambiguity (e.g., entangling camera and agent poses) and topological failures (e.g., missing objects), causing severe rendering artifacts. To enable robust in-the-wild reconstruction, we introduce Adaptive Gaussian Graph (AGG), a self-correcting 4D fr...
| Authors | Xiaoyun Dong, Qian Xu, Yun Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 July 2026 |
| Last updated at source | 29 July 2026 |
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