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.

Y et, despite this progress, the point clouds acquired by MLS systems operating in real-world environments inevitably contain uncertainty arising from various error sources during acquisition and processing. Although MLS systems have advanced rapidly in both data collection and post-processing, research on uncertainty evaluation has received comparatively less attention and remains underdeveloped (Xu et al., 2025b). From a user's perspective, the quality of point clouds from MLS systems is a critical concern. As the foundational input for many downstream tasks, inadequate assessment of MLS point clouds' quality can easily impact high-precision applications such as navigation and change analysis. This will not only undermine reliability but also result in substantial waste of time and resources, which is unacceptable in real-world applications. There is a clear need for automated and reliable solutions for uncertainty evaluation. In MLS systems, four main categories of error sources contribute to uncertainty: instrumental errors, atmospheric errors, object-and geometry-related errors, and trajectory estimation errors (Habib et al., 2009, Schenk, 2001). Considering the characteristics of these error sources, existing uncertainty evaluation methods can be broadly divided into two categories: forward modeling and backward modeling (Shi et al., 2021). The core idea of forward modeling is grounded in variance-covariance propagation, which involves detailed theoretical analysis of MLS system errors.

In the reconstruction of fa\c{c}ade elements, the identification of specific object types remains challenging and is often circumvented by rectangularity assumptions or the use of bounding boxes. We propose a new approach for the reconstruction of 3D fa\c{c}ade details. We combine MLS point clouds and a pre-defined 3D model library using a BoW concept, which we augment by incorporating semi-global features. We conduct experiments on the models superimposed with random noise and on the TUM-FA\c{C}ADE dataset. Our method demonstrates promising results, improving the conventional BoW approach. It holds the potential to be utilized for more realistic facade reconstruction without rectangularity assumptions, which can be used in applications such as testing automated driving functions or estimating fa\c{c}ade solar potential.