论文标题
关于瑞士3个城市的语义细分：航空摄影测量3D PointCloud数据集的基准研究
Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset
论文作者
论文摘要
我们介绍了一个新的户外Urban 3D PointCloud数据集，总面积为2.7 $ km^2 $，从三个具有不同特征的瑞士城市采样。该数据集是用每个点标签的语义分割的，并使用来自配备高分辨率摄像机的多旋转器获取的图像的摄影测量法构建。与使用接地激光雷达传感器获取的数据集相反，所得点云均匀密集且完整，并且对不同的应用程序有用，包括自主驾驶，游戏和智能城市规划。作为基准，我们报告了PointNet ++的定量结果，PointNet ++是基于点的深3D语义分割模型。在此模型上，我们还研究了使用不同城市进行模型概括的影响。
We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km^2$, 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.
