大规模点云处理以实现高效的建筑质量检查和控制
Zhansheng Liu1,2, Zehong Liu1,2, Zhe Sun1,3
1College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
优化土木工程数据处理涉及对空间时间数据进行人工智能算法的比较. 选择适当的点云采样,过和注册方法可以提高建筑质量控制的效率和准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 地理空间数据科学数据科学
- 建筑中的人工智能
背景情况:
- 大规模的民用基础设施项目产生了大量的时空数据集,用于管理和控制.
- 目前基于人工智能的数据处理需要工程师进行大量的手动参数调整,这对于大型数据集来说是低效的.
- 缺乏统一的数据集阻碍了对不同数据处理算法的比较分析.
研究的目的:
- 提出一个框架和评估系统,用于比较建筑质量控制中大规模时空数据的数据处理政策.
- 评估用于处理大规模点云数据的组合算法的性能.
- 根据注册准确性和数据保真性,确定最佳点云处理策略.
主要方法:
- 开发一个新的框架和评估系统,用于比较数据处理策略.
- 对点云算法 (采样,过,注册) 的各种组合进行比较分析.
- 基于注册准确性和数据忠实度指标的绩效评估.
主要成果:
- 证明了点云采样,过和注册算法的战略组合可以显著提高处理效率.
- 验证优化策略符合数据准确性和完整性的工程要求.
- 拟议的框架有效地评估和比较不同的数据处理政策.
结论:
- 选择合适的点云处理算法组合对于高效准确地处理土木工程中的大量时空数据至关重要.
- 开发的框架为优化建筑质量控制中的数据处理方法提供了一个系统的方法.
- 这项研究为处理大量点云数据和选择最佳处理技术提供了可扩展的解决方案.
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