Joint Detection and Parameters Regression of Detailed Windows based on Facade Textures via an Adaptive Soft Teacher

The detailed and precise reconstruction of building facades with semantic information conforming to the level-of-detail protocol has drawn increasing attention in recent years. However, despite windows being a major component of building facades, current research often oversimplifies them in the modeling process, primarily focusing on their location and size. This study proposes a joint approach to simultaneously address window detection and detailed parametric modeling. All required window information, including type, location, and interior structure parameters, is obtained via an end-to-end network. This information is then consolidated into predefined window syntax accessible via the SketchUp Ruby application programming interface (API), resulting in the construction of detailed three-dimensional window models. To resolve the issue of training the network with limited labeled data, an adaptive threshold method based on Soft Teacher is proposed, leveraging the pseudolabel technique and consistency regularization to enhance detection precision. Experiments performed on multiple datasets demonstrate that the proposed methods achieve improved window-detection precision with reduced cell estimation errors compared with existing Faster R‐CNN‐based methods. The mean average precision and average precision at the intersection of union = 0.5 (AP50) metrics increased from 47.9% and 61.9% to 69.0% and 83.1%, respectively. The mean absolute error metric for cell estimation improved from 0.103 to 0.076.