Classifying point clouds at the facade-level using geometric features and deep learning networks

• URL: http://arxiv.org/abs/2402.06506v1
• Date: Fri, 9 Feb 2024 16:14:30 GMT
• Title: Classifying point clouds at the facade-level using geometric features and deep learning networks
• Authors: Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
• Abstract summary: Classifying point clouds at facade-level is key to create such digital replicas of the real world. We propose a method fusing geometric features with deep learning networks for point cloud classification at facade-level.
• Score: 7.272181023476306
• License: http://creativecommons.org/licenses/by/4.0/
• 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' performance. This method can be applied for compensating deep learning networks' ability in capturing local geometric information and promoting the advancement of semantic segmentation.

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