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街道高斯:用高斯原始模型模拟动态的城市场景
IEEE transactions on pattern analysis and machine intelligence
|November 19, 2025
概括
街道高斯人为动态的城市驾驶场景提供了一种新的明确场景表示. 与现有的神经辐射场 (NeRF) 方法相比,这种方法显著提高了训练和染速度.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 3D场景表示 3D场景表示
背景情况:
- 模拟动态的城市街道场景对于自动驾驶至关重要.
- 像神经辐射场 (NeRF) 这样的现有方法提供了照片现实的合成,但受到了缓慢的训练和染.
- 速度限制阻碍了自动驾驶系统的实时应用和可扩展性.
研究的目的:
- 在动态城市场景建模中解决缓慢训练和染速度的局限性.
- 引入一种新的显式场景表示,以实现高效和高质量的视图合成.
- 为了使自动驾驶应用程序的场景编辑和染速度更快.
主要方法:
- 介绍了Street Gaussians,这是一个明确的场景表示,使用有语义逻辑和高斯原体的点云.
- 通过可优化的追踪姿势和4D球形波模型来模拟前景对象动态.
- 启用了对象和背景的简单组合,用于场景编辑和染.
主要成果:
- 在1066*1600分辨率下实现了135 FPS的染速度.
- 在不到半小时内完成培训.
- 在KITTI和Waymo开放数据集上表现出高于最先进的方法的性能.
结论:
- 街道高斯为自动驾驶提供了一个高效和有效的解决方案,用于建模动态的城市场景.
- 显式表示方便了更快的训练,染和场景编辑.
- 与以前的方法相比,该方法显示了性能和速度的显著改善.
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