对室内建筑物采样密度的效率评估 LiDAR 点云细分点云细分
Yiquan Zou1, Wenxuan Chen1, Tianxiang Liang1
1School of Civil Engineering, Architecture and the Environment, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一个统一的框架,以优化LiDAR点云采样密度用于室内测绘. 它确定了一个理想的密度范围,用于在Scan-to-BIM工作流程中平衡精度和效率.
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
- 地理学工程 工程地质学
背景情况:
- 室内LiDAR点云语义细分精度受到采样密度的严重影响,造成了精度-效率的权衡.
- 目前的密度选择方法通常是启发式的,缺乏标准化的协议,限制了实际应用的定量指导.
研究的目的:
- 开发一个统一的评估框架来分析采样密度对LiDAR点云语义细分的影响.
- 为优化室内映射和Scan-to-BIM (扫描到建筑信息建模) 工作流程中的密度提供可重复的,无模型的指导.
主要方法:
- 一个标准化的协议被用于评估三个代表性骨干 (PointNet,PointNet++,DGCNN),并增加了一个点变压器模块.
- 该框架采用同位素的voxel引导的统一下方采样和决策规则,整合准确度足够性,效率和准确度密度曲线分析.
- 在室内点云上进行了实验,将扫描数据与BIM数据进行了对比.
主要成果:
- 该研究量化了准确性运行时间的权衡,确定了工程可行的1600-2900点/平方米的操作频段,稳健设置约为2400点/平方米.
- 平面元件在中等密度时显示和,而光束元件对向下采样更敏感.
- 拟议的框架通过隔离密度效应和标准化评估协议,提供可重复的指导.
结论:
- 这项研究为室内绘图中的扫描计划和计算预算提供了定量,可重复和模型不可知性的指导.
- 这些发现对于优化资源配置和提高扫描到BIM流程效率至关重要.
- 建立一种标准化的密度选择方法可以提高基于LiDAR的室内测绘解决方案的可靠性和适用性.
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