城市基因:具有组合性和可控制的神经场的城市生成.
IEEE transactions on pattern analysis and machine intelligence
|August 19, 2025
概括
UrbanGen生成复杂的城市辐射场,具有光现实的染和精确的几何. 这种方法使用3D全视图预先进行增强控制和多样化的城市风格,优于现有的生成辐射场方法.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 生成辐射场已经迅速发展,但与复杂的城市场景生成斗争.
- 现有的方法主要专注于以对象为中心的应用程序,缺乏用于复杂城市环境的功能.
研究的目的:
- 推出UrbanGen,这是一种创新的解决方案,用于生成高保真度的城市辐射场.
- 为了在复杂的城市场景中实现光现实的染,精确的几何形状和高可控性.
- 为了实现多样化的城市风格生成和编辑.
主要方法:
- 利用粗的3D全视前景 (物品的语义语音格子,对象的边界框) 来调节一个组成的生成辐射场.
- 使用语义和几何损失与对抗训练相结合,以遵守输入条件.
- 从KITTI-360,nuScenes和Waymo开发一个统一的数据集,结合全视图先验来训练一个城市风格意识模型.
主要成果:
- 在图像保真和几何精度方面,UrbanGen在城市场景生成方面表现出高于最先进的基线的卓越性能.
- 该方法成功地产生了城市辐射场,具有光现实的染和准确的几何表示.
- 实现了RGB图像,语义地图和深度地图的联合染.
结论:
- UrbanGen有效地解决了生成复杂的城市辐射场的挑战.
- 拟议的全视眼前景允许对场景元素和风格进行解和多功能控制.
- UrbanGen引入了新的可控性功能,包括大型摄像机移动,内容编辑和城市风格操纵,推进了生成场景合成领域.
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