@inproceedings{4d0048813d4f4a06aaa8a66f9a51986b,
title = "Classifying Point Clouds at the Facade-Level Using Geometric Features and Deep Learning Networks",
abstract = "3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of the real world. However, few studies have focused on such detailed classification with deep neural networks. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level. Our experiments conclude that such early-fused features improve deep learning methods{\textquoteright} performance. This method can be applied for compensating deep learning networks{\textquoteright} ability in capturing local geometric information and promoting the advancement of semantic segmentation.",
keywords = "Deep learning, Geometric features, Point cloud classification",
author = "Yue Tan and Olaf Wysocki and Ludwig Hoegner and Uwe Stilla",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International 3D GeoInfo Conference, 3DGeoInfo 2023 ; Conference date: 12-09-2023 Through 14-09-2023",
year = "2024",
doi = "10.1007/978-3-031-43699-4_25",
language = "English",
isbn = "9783031436987",
series = "Lecture Notes in Geoinformation and Cartography",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "391--404",
editor = "Kolbe, {Thomas H.} and Andreas Donaubauer and Christof Beil",
booktitle = "Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference",
}