高效重建城市级场景，实时渲染且抗视角外观差异。
Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction
- 分块并行训练+可视性选图，提升训练效率。
- 可控细节层级策略，在预算内保持高画质。
- 抗外观不一致，适合大规模城市重建应用。
我们提出一种框架，实现城市级场景的快速重建与实时渲染，同时对多视角捕捉中的外观变化具有鲁棒性。方法首先通过场景分割实现并行训练，采用基于可视性的图像选择策略优化训练效率。引入可控制的细节层级（LOD）策略，在用户定义的预算下显式调节高斯密度，实现高效训练与渲染的同时保持高视觉保真度。外观变换模块减轻图像间外观不一致的负面影响，并支持灵活调整。此外，通过深度正则化、尺度正则化和抗锯齿等增强模块，进一步提升重建精度。实验表明，该方法能有效重建城市级场景，在效率与质量上均优于现有方法。源代码见：https://yzslab.github.io/REUrbanGS。
原文摘要 · Abstract (English)
We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize training efficiency. A controllable level-of-detail (LOD) strategy explicitly regulates Gaussian density under a user-defined budget, enabling efficient training and rendering while maintaining high visual fidelity. The appearance transformation module mitigates the negative effects of appearance inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and antialiasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality. The source code is available at: https://yzslab.github.io/REUrbanGS.
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