We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of $2.7{\text{km}}^2$, sampled from three Swiss cities with different characteristics. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras. In contrast to datasets acquired with ground LiDAR sensors, the resulting point clouds are uniformly dense and complete, and are useful to disparate applications, including autonomous driving, gaming and smart city planning. As a benchmark, we report quantitative results of PointNet++, an established point-based deep 3D semantic segmentation model; on this model, we additionally study the impact of using different cities for model generalization.

我们引入了一个新的室外城市 3D 点云数据集，覆盖总面积为 $2.7{\text{公里}}^2$，来自三个不同特色的瑞士城市。该数据集使用每点标签手动注释以进行语义分割，并使用摄影测量法从配备高分辨率相机的多旋翼获取的图像中构建。与使用地面 LiDAR 传感器获取的数据集相比，生成的点云均匀密集且完整，可用于不同的应用，包括自动驾驶、游戏和智慧城市规划。作为基准，我们报告了 PointNet++ 的定量结果，PointNet++ 是一种已建立的基于点的深度 3D 语义分割模型；在这个模型上，我们还研究了使用不同城市进行模型泛化的影响。