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Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds

Ziyang Xu, Olaf Wysocki, Christoph Holst

TL;DR

The paper tackles the challenge of scalable uncertainty quantification in Mobile Laser Scanning (MLS) point clouds by learning point-level errors from local geometric features. It introduces a data-driven framework that casts uncertainty prediction as a binary classification task using Random Forest and XGBoost, trained on spatially partitioned data to prevent leakage. Empirical results show ROC-AUC values around 0.87–0.88 and highlight elevation variation, point density, and local complexity as key predictive features, with strong cross-model feature agreement. The contributions provide a scalable foundation for MLS quality control and error analysis, reducing dependence on costly high-precision references and enabling adaptable uncertainty evaluation for large-scale point clouds.

Abstract

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.

Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds

TL;DR

The paper tackles the challenge of scalable uncertainty quantification in Mobile Laser Scanning (MLS) point clouds by learning point-level errors from local geometric features. It introduces a data-driven framework that casts uncertainty prediction as a binary classification task using Random Forest and XGBoost, trained on spatially partitioned data to prevent leakage. Empirical results show ROC-AUC values around 0.87–0.88 and highlight elevation variation, point density, and local complexity as key predictive features, with strong cross-model feature agreement. The contributions provide a scalable foundation for MLS quality control and error analysis, reducing dependence on costly high-precision references and enabling adaptable uncertainty evaluation for large-scale point clouds.

Abstract

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.

Paper Structure

This paper contains 14 sections, 4 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Uncertainty Metric Definition
  4. Feature Extraction
  5. Model Selection
  6. Evaluation and Validation
  7. Experiment
  8. Data Preparation
  9. Model Training
  10. Results Analysis
  11. Discussion
  12. Advantages
  13. Challenges
  14. Conclusion

Figures (4)

  • Figure 1: Workflow of the proposed machine learning-based uncertainty evaluation framework.
  • Figure 2: Overview of the scanned area.
  • Figure 3: Comparison of the top 20 most important features.
  • Figure 4: Correlation between feature importance scores of two models. For better visualization, the axes of RF and XGBoost are unequal.