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.

翻译：具有立面细节的三维建筑模型在众多应用中正发挥着重要作用。实现建筑立面级别的点云分类是创建此类真实世界数字副本的关键。然而，目前鲜有研究利用深度神经网络进行如此精细的分类。我们提出了一种融合几何特征与深度学习网络的建筑立面级点云分类方法。实验表明，这种早期融合的特征能够提升深度学习方法的性能。该方法可用于弥补深度学习网络在捕捉局部几何信息方面的不足，并推动语义分割技术的发展。