Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset

Abstract

Three-dimensional (3D) city modeling is an essential component of 3D geoscience modeling, and window detection of building facades plays a crucial role in 3D city modeling. Windows can serve as structural priors for rapid building reconstruction. In this article, we propose a framework for detecting window lines. The framework consists of two parts: an improved stacked hourglass network and a point–line extraction module. This framework can output vectorized window wireframes from building facade images. Besides, our method is end-to-end trainable, and the vectorized window wireframe consists of point–line structures. The point–line structure contains both semantic and geometric information. Additionally, we propose a new dataset of real-world building facades for window-line detection. Our experimental results demonstrate that our proposed method has superior efficiency, accuracy, and applicability in window-line detection compared to existing line detection algorithms. Moreover, our proposed method presents a new idea for deep learning methods in window detection and other application scenarios in current 3D geoscience modeling.

Keywords

• 3d geoscience modeling
• 3d city modeling
• hourglass network
• deep learning
• building façade
• window line
• vectorized wireframe