Deep Learning-Based Fa?ade Semantic Extraction From Street-Level Images

Fa?ade semantic information can be calculated,analysed and interacted in 3D building models.It plays an important role in the fields of smart city,virtual reality and autonomous driving,and start to be regarded as important independent information for other practical applications.Existing model-driven methods for fa?ade parsing can obtain fa?ade elements with great structure but rely on prior knowledge.Hence,they are difficult to be applied to buildings with different architectural styles.Data-driven methods achieve fa?ade parsing based on fa?ade element features automatically learned from dataset,rather than man-made grammar rules.However,the structures of fa?ade elements from them are often incomplete because these methods usually get a pixelwise results.At the same time,both of these methods highly rely on the dataset to obtain prior knowledge or fa?ade element features.Existing fa?ade semantic datasets only have one single front view,good contrast,and no background.Methods designed and tested based on them cannot be applied to the practical world with more complex scenes.Aiming to close this research gap and enable the fa?ade parsing method to be applied in the real world,in this paper,we explored how to improve fa?ade semantic dataset and fa?ade parsing methods:(1)We built a large street-level fa?ade semantic dataset by taking Mapillary images with more general scenes.(2)At the same time,based on the feature analysis result of every-class fa?ade elements,we proposed a new modular convolutional neural network(CNN)pipeline.Our pipeline combined pixelwise segmentation and global object detection to achieve fa?ade parsing with better results.The result of ablation study demonstrates the effectiveness of our pipeline’s design.Our pipeline also achieves state-of-the-art results in the qualitative and quantitative experiments on the standard fa?ade semantic dataset and our new street-level fa?ade semantic dataset.These experimental results demonstrate that our pipeline can be applied to facades with complex scenes.In addition,the generalization experiment demonstrates the good generalization of our pipeline.