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.

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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
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article

Fine-Grained 3-D Reconstruction of Urban Building Façades via Window-Driven Semantic Parsing

Yuejin Li, Shuixin Ye, Shijie Zhang, Zicheng Qin et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote Sensing and LiDAR Applications
article

Fine-Grained 3-D Reconstruction of Urban Building Façades via Window-Driven Semantic Parsing

Yuejin Li, Shuixin Ye, Shijie Zhang, Zicheng Qin, Junhong He, Jian Yang
article en

Abstract

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.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 19
Shenzhen Technology University (CN), GeoInformation (United Kingdom) (GB), Shenzhen MSU-BIT University
Sustainable cities and communities
Openalex Percentile: Top 43%
Remote Sensing and LiDAR Applications
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