TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System

TL;DR

TS40K introduces a large-scale 3D point cloud dataset of European rural electrical transmission systems, addressing a novel domain with unique data characteristics for advancing 3D scene understanding.

Contribution

The paper presents TS40K, a new dataset with over 40,000 km of rural transmission data, annotated with 22 classes, and evaluates current methods on this challenging new domain.

Findings

State-of-the-art methods show varied performance on TS40K.

High point-density and lack of occlusion present unique challenges.

Labels not originally designed for learning tasks impact results.

Abstract

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.