From Propagation to Prediction: Point-level Uncertainty Evaluation of MLS Point Clouds under Limited Ground Truth

TL;DR

This paper introduces a learning-based framework that predicts point-level uncertainty in MLS point clouds using geometric features, reducing reliance on costly ground truth data and demonstrating comparable accuracy with improved efficiency.

Contribution

It presents a novel learning-based approach for uncertainty evaluation in MLS point clouds that does not depend heavily on ground truth data.

Findings

The framework is feasible on real-world data.

XGBoost achieves similar accuracy to Random Forest.

The method is about three times faster than traditional approaches.

Abstract

Evaluating uncertainty is critical for reliable use of Mobile Laser Scanning (MLS) point clouds in many high-precision applications such as Scan-to-BIM, deformation analysis, and 3D modeling. However, obtaining the ground truth (GT) for evaluation is often costly and infeasible in many real-world applications. To reduce this long-standing reliance on GT in uncertainty evaluation research, this study presents a learning-based framework for MLS point clouds that integrates optimal neighborhood estimation with geometric feature extraction. Experiments on a real-world dataset show that the proposed framework is feasible and the XGBoost model delivers fully comparable accuracy to Random Forest while achieving substantially higher efficiency (about 3 times faster), providing initial evidence that geometric features can be used to predict point-level uncertainty quantified by the C2C distance. In summary, this study shows that MLS point clouds' uncertainty is learnable, offering a novel learning-based viewpoint towards uncertainty evaluation research.