Abstract
Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a multi-style street simulator that achieves instruction-driven scene editing with guaranteed spatial and temporal consistency. Building on a state-of-the-art Gaussian Splatting framework for street scenarios enhanced by our proposed pose optimization and multi-view training, our method enables photorealistic style transfers across seasons, weather conditions, and camera setups through three key innovations: First, a hybrid embedding scheme disentangles persistent scene geometry from transient style attributes, allowing realistic environmental edits while preserving structural integrity. Second, uncertainty-aware rendering mitigates supervision noise from diffusion priors, enabling robust training across extreme style variations. Third, a unified parametric model prevents geometric drift through regularized updates, maintaining multi-view consistency across seven vehicle-mounted cameras. Our framework preserves the original scene's motion patterns and geometric relationships. Qualitative results demonstrate plausible transitions between diverse conditions (snow, sandstorm, night), while quantitative evaluations show state-of-the-art geometric accuracy under style transfers. The approach establishes new capabilities for urban simulation, with applications in autonomous vehicle testing and augmented reality systems requiring reliable environmental consistency. Codes will be publicly available upon publication.
Abstract (translated)
城市场景重建需要同时建模静态基础设施和动态元素，并支持各种环境条件。我们介绍了**StyledStreets**，这是一个多风格街景模拟器，它通过保证空间和时间一致性来实现指令驱动的场景编辑。我们的方法基于最先进的高斯点绘框架进行街景建模，并在此基础上提出了姿态优化和多视角训练，使模型能够跨季节、天气条件以及摄像机设置进行逼真的风格转换。以下是三个关键创新： 1. **混合嵌入方案**：该方案将持久的场景几何与瞬时的样式属性分离，允许在保留结构完整性的前提下对环境做出真实感的编辑。 2. **不确定性感知渲染**：这种方法减轻了来自扩散先验的监督噪声，使得模型能够在极端风格变化的情况下进行稳健训练。 3. **统一参数化模型**：通过正则化更新防止几何漂移，并保持七个车载摄像头视角的一致性。 我们的框架保留了原始场景的运动模式和几何关系。定性的结果展示了在不同条件（如雪天、沙尘暴、夜晚）之间合理的过渡，而定量评估显示，在风格转换过程中达到了最先进的几何准确性。该方法为城市模拟建立了一系列新能力，适用于自动驾驶汽车测试以及需要可靠环境一致性的增强现实系统。相关代码将在论文发表后公开提供。
URL
https://arxiv.org/abs/2503.21104
