Classifying point clouds at the facade-level using geometric features and deep learning networks
This work addresses the need for detailed 3D building models in applications like digital replicas, but it is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of classifying point clouds at the facade-level by fusing geometric features with deep learning networks, resulting in improved performance for deep learning methods in capturing local geometric information.
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.