基于人工智能的民用基础设施3D建模策略:对NeRF和摄像度的定量评估
Edison Atencio1, Fabrizzio Duarte1, Fidel Lozano-Galant2
1School of Civil Engineering, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
神经辐射场 (NeRFs) 为土木工程提供快速,光现实的3D模型,图像较少,而数字光谱学则在准确性和完整性方面表现出色. 这种比较有助于选择用于基础设施监控的技术.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 地理学就是地质学.
背景情况:
- 三维 (3D) 建模对于基础设施分析,监督和规划至关重要.
- 用于3D建模的图像采集可以在操作上复杂,增加现场工作时间和成本.
- 在数据限制下评估重建技术对于高效的基础设施监控至关重要.
研究的目的:
- 为了比较评估神经辐射场 (NeRFs) 和数字摄像度用于3D基础设施建模.
- 在不同的数据可用性和图像采集条件下分析NeRF和光谱的性能.
- 根据工程目标和资源限制,制定一个选择适当3D建模技术的框架.
主要方法:
- 使用了巴尔帕莱索天主教大学 (Pontificia Universidad Católica de Valparaíso) 一个露台的现实世界案例研究.
- 通过逐渐减少的图像数据集,评估了NeRF和数字光谱.
- 性能指标包括视觉质量,点云密度,几何精度和处理时间.
主要成果:
- NeRF提供了快速,光现实的3D重建,使用更少的图像.
- 数字摄影仪表显示出卓越的度量精度和结构完整性.
- 随着数据减少,NeRF的效率支持在可变条件下的基础设施监控.
结论:
- 在有限的数据中,NeRF在速度和视觉质量方面表现出色,而光谱仪在几何精度方面领先.
- 面向应用程序的框架和混合工作流可以指导选择3D建模技术.
- 像NeRF这样的基于AI的重建方法可以增强数字工作流程,以限制数据监控基础设施.
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