  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.
