用机器学习评估移动激光点云的点级不确定性，无需高精度参考数据。
Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds
- 基于局部几何特征与误差的非线性关系，训练随机森林和XGBoost模型。
- 平均ROC-AUC超过0.87，证明模型能有效预测点云不确定性。
- 适用于大尺度点云的质量控制，适合3D建模与变化分析场景。
移动激光扫描（MLS）点云的不确定性可靠量化对三维地图、建模和变化分析等下游应用的准确性与可信度至关重要。传统反向不确定性建模高度依赖高精度参考数据，但在大规模场景下往往成本过高或难以获取。为此，本文提出一种基于机器学习的点级不确定性评估框架，通过学习局部几何特征与点级误差之间的关系实现。该框架采用随机森林（RF）和XGBoost两种集成学习模型，在空间划分的真实数据集上训练与验证，避免数据泄露。实验表明，两种模型均能有效捕捉几何特征与不确定性间的非线性关系，平均ROC-AUC值高于0.87。分析显示，高程变化、点密度及局部结构复杂度等几何特征在不确定性预测中起主导作用。该框架提供了数据驱动的不确定性评估视角，为大规模点云的质量控制与误差分析提供可扩展、可适应的基础。
原文摘要 · Abstract (English)
Reliable quantification of uncertainty in Mobile Laser Scanning (MLS) point clouds is essential for ensuring the accuracy and credibility of downstream applications such as 3D mapping, modeling, and change analysis. Traditional backward uncertainty modeling heavily rely on high-precision reference data, which are often costly or infeasible to obtain at large scales. To address this issue, this study proposes a machine learning-based framework for point-level uncertainty evaluation that learns the relationship between local geometric features and point-level errors. The framework is implemented using two ensemble learning models, Random Forest (RF) and XGBoost, which are trained and validated on a spatially partitioned real-world dataset to avoid data leakage. Experimental results demonstrate that both models can effectively capture the nonlinear relationships between geometric characteristics and uncertainty, achieving mean ROC-AUC values above 0.87. The analysis further reveals that geometric features describing elevation variation, point density, and local structural complexity play a dominant role in predicting uncertainty. The proposed framework offers a data-driven perspective of uncertainty evaluation, providing a scalable and adaptable foundation for future quality control and error analysis of large-scale point clouds.
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