一种基于神经场的方法,用于3D城市环境中的视图计算和数据探索
IEEE transactions on visualization and computer graphics
|November 21, 2025
概括
本研究介绍了一种神经场方法,用于高效的3D城市数据探索,克服计算挑战和阻塞问题,以改善大规模分析和观点发现.
科学领域:
- 计算机视觉 计算机视觉
- 城市信息学 城市信息学
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 3D城市数据集越来越多,但由于计算限制和复杂的几何形状,难以分析.
- 遮蔽和手动视角调整阻碍了对大规模3D城市环境的有效探索.
研究的目的:
- 开发一种基于视图的新方法,用于高效的3D城市数据探索.
- 介绍一种基于神经场的方法,用于隐式表示3D环境.
- 为了实现更快的数据查询,并促进最佳视角的搜索,解决阻塞问题.
主要方法:
- 一种基于视图的方法,使用矢量场来编码环境视图.
- 一种基于神经场的方法,用于创建3D城市数据的高效隐式表示.
- 直接查询用于视图评估,反向查询用于避免堵塞和匹配模式.
主要成果:
- 拟议的方法可以更快地对3D城市数据进行直接和反向查询.
- 有效地确定理想的观点,并分析城市规划任务的可见性.
- 在可见度评估,阳光照射评估和视觉影响分析方面取得了成功.
结论:
- 基于神经现场的方法显著提高了3D城市数据探索的效率和有效性.
- 该方法支持关键的城市分析任务,改善城市规划和发展的决策.
- 公共可用的代码和数据促进了更广泛的采用和该领域的进一步研究.
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