用几何特征预测点云不确定性，无需真实标签也能评估精度。
From Propagation to Prediction: Point-level Uncertainty Evaluation of MLS Point Clouds under Limited Ground Truth
- 结合最优邻域与几何特征，构建学习框架预测点级不确定性。
- XGBoost模型速度比随机森林快约3倍，精度相当。
- 适合需要高效评估点云质量的工程应用，如建筑建模和形变分析。
在扫描到BIM、形变分析和三维建模等高精度应用中，移动激光扫描（MLS）点云的不确定性评估至关重要。然而，获取真实标签（GT）常成本高昂且不切实际。为减少对真实标签的依赖，本文提出一种基于学习的框架，融合最优邻域估计与几何特征提取。在真实数据集上的实验表明，该框架可行，且XGBoost模型在精度上与随机森林相当，效率提升约3倍，初步验证了几何特征可有效预测由点到点（C2C）距离量化出的点级不确定性。结果表明，MLS点云的不确定性具有可学习性，为不确定性评估研究提供了新的学习视角。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。