A Semantics-aware Method for Adding 3D Window Details to Textured LoD2 CityGML Models

3D window details for buildings are important in many 3D simulation and visualization applications. However, they are not easy to acquire or reconstruct. Thus, many 3D city models have no 3D windows, but only 2D planar textures for their façades (i.e., textured LoD2 CityGML models). Many procedural methods have been proposed to generate 3D façade details from images. However, they usually require tedious efforts to create a procedural grammar to achieve desired results, and lack consideration of window semantics which is a useful building property. In this paper, we propose a novel semantics-aware method for adding 3D window details to textured LoD2 CityGML models. We propose a two-level deep learning-based windowpane detection followed by processing and adjusting the detection results then generating and adding 3D windows to the building models. Different from existing methods, we focus on adding window details considering the semantics (i.e., frames and panes). Moreover, our method does not require tedious reconstruction or grammar creation efforts. It extracts the information present in the texture itself only, finds and adjusts the patterns and shapes from the detection results in an unsupervised and efficient manner to achieve neat window parsing results. Specifically, we propose clustering-based window/pane alignment, neatness-based window image voting, grid-based symmetry and thickness filtering, and fitting-based window-top modeling. Experiments on representative 3D city datasets and illustrative applications demonstrate the effectiveness and usefulness of our method.