TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System
This dataset addresses the lack of openly available 3D data for rural and power-grid inspection scenarios, which is a novel problem for the research community and can aid in high-risk missions.
The authors introduced TS40K, a 3D point cloud dataset with over 40,000 km of European rural terrain and electrical transmission systems, labeled with 22 classes, and evaluated state-of-the-art methods for 3D semantic segmentation and object detection on it.
Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force of this research is the problem of autonomous driving followed by indoor scene segmentation. However, openly available 3D data on these tasks mainly focuses on urban scenarios. In this paper, we propose TS40K, a 3D point cloud dataset that encompasses more than 40,000 Km on electrical transmission systems situated in European rural terrain. This is not only a novel problem for the research community that can aid in the high-risk mission of power-grid inspection, but it also offers 3D point clouds with distinct characteristics from those in self-driving and indoor 3D data, such as high point-density and no occlusion. In our dataset, each 3D point is labeled with 1 out of 22 annotated classes. We evaluate the performance of state-of-the-art methods on our dataset concerning 3D semantic segmentation and 3D object detection. Finally, we provide a comprehensive analysis of the results along with key challenges such as using labels that were not originally intended for learning tasks.