用3D模型自动生成室内激光点云语义标签，提升标注效率与精度。
3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model
- 基于3D CAD模型自动对齐生成伪标签，替代人工标注。
- 伪标签准确率超95%，显著降低深度模型训练时间。
- 适合建筑信息建模与机器人导航研究者使用。
室内点云语义分割在数字孪生、机器人导航和建筑信息建模（BIM）中应用广泛。然而，现有标注数据集多通过摄影测量获取。相比之下，地面激光扫描（TLS）可捕获密集的亚厘米级点云，已成为测绘标准。我们提出3DSES（3D Segmentation of ESGT point clouds），一个覆盖427平方米工程学院建筑的高密度彩色激光点云数据集。该数据集具有双重标注：点级别语义标签与完整的3D CAD模型。我们引入一种模型到点云对齐算法，利用现有3D CAD模型自动化生成点云伪标签。3DSES包含三种不同语义与几何复杂度的变体。实验表明，该对齐方法生成的伪标签准确率超过95%，使深度模型训练时间大幅减少。初步基线结果揭示现有模型在分割与BIM相关的照明及安全设施等物体时仍存困难。我们发现，结合伪标签与激光强度信息（当前数据集罕见使用）可进一步提升分割精度。代码与数据将开源。
原文摘要 · Abstract (English)
Semantic segmentation of indoor point clouds has found various applications in the creation of digital twins for robotics, navigation and building information modeling (BIM). However, most existing datasets of labeled indoor point clouds have been acquired by photogrammetry. In contrast, Terrestrial Laser Scanning (TLS) can acquire dense sub-centimeter point clouds and has become the standard for surveyors. We present 3DSES (3D Segmentation of ESGT point clouds), a new dataset of indoor dense TLS colorized point clouds covering 427 m 2 of an engineering school. 3DSES has a unique double annotation format: semantic labels annotated at the point level alongside a full 3D CAD model of the building. We introduce a model-to-cloud algorithm for automated labeling of indoor point clouds using an existing 3D CAD model. 3DSES has 3 variants of various semantic and geometrical complexities. We show that our model-to-cloud alignment can produce pseudo-labels on our point clouds with a \> 95% accuracy, allowing us to train deep models with significant time savings compared to manual labeling. First baselines on 3DSES show the difficulties encountered by existing models when segmenting objects relevant to BIM, such as light and safety utilities. We show that segmentation accuracy can be improved by leveraging pseudo-labels and Lidar intensity, an information rarely considered in current datasets. Code and data will be open sourced.
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