Learning Under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems
Abstract
Research in 3D scene understanding particularly in autonomous driving and indoor segmentation has made significant strides. However most available datasets focus on urban settings. We introduce TS40K a 3D point cloud dataset spanning 40000 km of electrical transmission systems in rural terrain addressing power-grid inspections to prevent outages damages and fires. TS40K offers high point density and no occlusion presenting challenges like noisy labels diverse structures and sensor noise causing spurious points. We evaluate state-of-the-art methods on 3D semantic segmentation and object detection revealing limitations in power grid inspection. TS40K invites further research to tackle these challenges. Resources available in: https://github.com/dlavado/TS40K
Cite
Text
Lavado et al. "Learning Under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems." Winter Conference on Applications of Computer Vision, 2025.Markdown
[Lavado et al. "Learning Under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/lavado2025wacv-learning/)BibTeX
@inproceedings{lavado2025wacv-learning,
title = {{Learning Under Noisy Labels Spurious Points and Diverse Structures: TS40K a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems}},
author = {Lavado, Diogo and Santos, Ricardo and Coelho, André and Santos, João and Micheletti, Alessandra and Soares, Cláudia},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2025},
pages = {7326-7336},
url = {https://mlanthology.org/wacv/2025/lavado2025wacv-learning/}
}