Classifying point clouds at the facade-level using geometric features
  and deep learning networks
Yue Tan Olaf Wysocki Ludwig Hoegner Uwe Stilla
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.