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

2024/02/09 by Yue Tan, Olaf Wysocki, Tan, Yue +5 · 1 citation
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Artificial Intelligence (cs.AI) #BIM and Construction Integration #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote Sensing and LiDAR Applications

paper · pdf · doi:10.48550/arxiv.2402.06506

openalex publication_date 2024/02/09 · openalex created_date 2024/02/13 · openalex updated_date 2026/07/28

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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