From Propagation to Prediction: Point-level Uncertainty Evaluation of MLS Point Clouds under Limited Ground Truth
This work addresses the challenge of uncertainty evaluation for MLS point clouds in high-precision applications like Scan-to-BIM, offering a novel learning-based approach that reduces reliance on ground truth, though it appears incremental as it builds on existing methods like XGBoost and Random Forest.
The study tackled the problem of evaluating uncertainty in Mobile Laser Scanning (MLS) point clouds without relying on costly ground truth, by proposing a learning-based framework that integrates optimal neighborhood estimation and geometric feature extraction. Experiments showed the XGBoost model achieved comparable accuracy to Random Forest while being about 3 times faster, indicating that geometric features can predict point-level uncertainty.
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.