# Classifying point clouds at the facade-level using geometric features and deep learning networks Source: [Classifying point clouds at the facade-level using geometric features and deep learning networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:20:14.513Z Authors: Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla Published: 2024-02-09T16:14:30.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()