透过LiDAR视角：面向地面点云分割的特征增强与不确定性感知标注流程

摘要

地面激光扫描（TLS）点云的精确语义分割受限于昂贵的手动标注成本。我们提出了一种半自动化、不确定性感知的流程，该流程集成了球面投影、特征增强、集成学习和定向标注，以减少标注工作量，同时保持高精度。我们的方法将三维点投影到二维球面网格上，通过多源特征丰富像素信息，并训练一组分割网络以生成伪标签和不确定性图，后者用于指导模糊区域的标注。二维输出被反投影回三维空间，生成密集标注的点云，并辅以三层可视化套件（二维特征图、三维着色点云和紧凑虚拟球体）以实现快速分类和审阅指导。利用这一流程，我们构建了Mangrove3D，一个针对红树林的语义分割TLS数据集。我们进一步评估了数据效率和特征重要性，以解决两个关键问题：（1）需要多少标注数据，（2）哪些特征最为重要。结果表明，性能在约12次标注扫描后趋于饱和，几何特征贡献最大，紧凑的九通道堆叠几乎捕捉了所有判别力，平均交并比（mIoU）稳定在约0.76。最后，通过在ForestSemantic和Semantic3D上的跨数据集测试，我们验证了特征增强策略的泛化能力。我们的贡献包括：（i）一个稳健的、不确定性感知的TLS标注流程及可视化工具；（ii）Mangrove3D数据集；以及（iii）关于数据效率和特征重要性的实证指导，从而为生态监测及其他领域实现可扩展、高质量的TLS点云分割提供了可能。数据集和处理脚本已公开于https://fz-rit.github.io/through-the-lidars-eye/。

English

Accurate semantic segmentation of terrestrial laser scanning (TLS) point clouds is limited by costly manual annotation. We propose a semi-automated, uncertainty-aware pipeline that integrates spherical projection, feature enrichment, ensemble learning, and targeted annotation to reduce labeling effort, while sustaining high accuracy. Our approach projects 3D points to a 2D spherical grid, enriches pixels with multi-source features, and trains an ensemble of segmentation networks to produce pseudo-labels and uncertainty maps, the latter guiding annotation of ambiguous regions. The 2D outputs are back-projected to 3D, yielding densely annotated point clouds supported by a three-tier visualization suite (2D feature maps, 3D colorized point clouds, and compact virtual spheres) for rapid triage and reviewer guidance. Using this pipeline, we build Mangrove3D, a semantic segmentation TLS dataset for mangrove forests. We further evaluate data efficiency and feature importance to address two key questions: (1) how much annotated data are needed and (2) which features matter most. Results show that performance saturates after ~12 annotated scans, geometric features contribute the most, and compact nine-channel stacks capture nearly all discriminative power, with the mean Intersection over Union (mIoU) plateauing at around 0.76. Finally, we confirm the generalization of our feature-enrichment strategy through cross-dataset tests on ForestSemantic and Semantic3D. Our contributions include: (i) a robust, uncertainty-aware TLS annotation pipeline with visualization tools; (ii) the Mangrove3D dataset; and (iii) empirical guidance on data efficiency and feature importance, thus enabling scalable, high-quality segmentation of TLS point clouds for ecological monitoring and beyond. The dataset and processing scripts are publicly available at https://fz-rit.github.io/through-the-lidars-eye/.

透过LiDAR视角：面向地面点云分割的特征增强与不确定性感知标注流程

Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation

摘要

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