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' performance. This method can be applied for compensating deep learning networks' ability in capturing local geometric information and promoting the advancement of semantic segmentation.
@article{arxiv.2402.06506,
title = {Classifying point clouds at the facade-level using geometric features and deep learning networks},
author = {Yue Tan and Olaf Wysocki and Ludwig Hoegner and Uwe Stilla},
journal= {arXiv preprint arXiv:2402.06506},
year = {2024}
}
Comments
Accepted to the Recent Advances in 3D Geoinformation Science, Proceedings of the 18th 3D GeoInfo Conference 2023