Fine-Grained 3-D Reconstruction of Urban Building Façades via Window-Driven Semantic Parsing
With the increasing demands of urban digitalization and the rise of low-altitude economy applications, building façade models are expected to evolve from basic geometric representations to detailed structures enriched with fine-grained semantic and structural information. This study proposes a fine-grained workflow for 3D modeling of urban building façades, integrating three key components: window detection and reconstruction, floor segmentation, and texture optimization. Specifically, (1) we develop a Bidirectional Adaptive Feature Fusion Network (BiFusion-Net) that robustly detects window positions and geometries by enhancing multi-scale feature fusion and semantic representation, effectively handling façade complexity and small target variations; (2) we reconstruct missing windows based on alignment patterns and design a rule-driven algorithm that leverages the vertical distribution of the completed window set to achieve automated and consistent floor segmentation; and (3) we implement a texture optimization module that enhances visual realism by correcting distortions and harmonizing façade colors through segmentation and inpainting techniques. The proposed approach was validated on a large-scale real-world 3D modeling project in Dangshan County. It enables the rapid generation of semantically enriched and visually realistic façade models at city scale, supporting applications such as drone-based logistics, emergency planning, and smart city operations. Experimental results show that the workflow achieves up to 97.38% F1-score in window detection, delivering superior performance compared with mainstream detectors. The optimized façades demonstrate improved visual consistency, as over 90% of wall regions exhibit clearer and more uniform color distributions. In the real-world project, the final reconstructed models achieve horizontal accuracy better than 0.3 m and vertical accuracy better than 0.2 m, fully meeting the standards for fine-grained urban 3D modeling.
Authors
- Yuejin Li
- Shuixin Ye
- Shijie Zhang (ORCID: https://orcid.org/0000-0002-9606-9955)
- Zicheng Qin
- Junhong He
- Jian Yang
Institutions
- Shenzhen Technology University (CN)
- GeoInformation (United Kingdom) (GB)
- Shenzhen MSU-BIT University
Publication Details
- Journal
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2025-12-23
- DOI
- https://doi.org/10.1109/jstars.2025.3647095
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00