FACTS: Training-free zero-shot diffusion framework for facade texture restoration in 3D urban models

DOI码：10.1016/j.aei.2026.104385

发表刊物：Advanced Engineering Informatics

摘要：High-fidelity facade texture restoration is crucial for the realism and utility of 3D urban models in digital twin applications. Low-quality textures can compromise visualization, simulation accuracy, and decision-making. This challenge is particularly evident in Level of Detail 1 and 2 (LoD-1 and LoD-2) models, which represent buildings as basic massing models. In these models, textures baked from complex 3D mesh sources often suffer from geometric distortions, occlusions, and inconsistent illumination. To address these issues, we introduce FACTS (Facade Automated Correction and Texture Synthesis), a novel zero-shot, training-free framework for facade texture restoration. FACTS operates as an automated pipeline, taking 3D Mesh as input and producing geometrically and photometrically corrected models. Its key innovations are as follows: (1) a prompt-guided, occlusion-aware inpainting module that uses semantic guidance to repair missing texture regions; (2) a multi-scale edge-feature-guided diffusion process that enforces geometric consistency by leveraging structural priors extracted from the image; and (3) an efficient illumination harmonization method in the CIELAB color space to resolve lighting inconsistencies across texture patches. Recognizing that conventional metrics fail to assess architectural integrity, we propose three novel metrics: the Edge Straightness Score (ESS), Hough Transform Line Consistency (HTLC), and Linearity Index (LI). Our experiments on the SFDB and RUF-3D datasets show significant improvements over baselines. Specifically, FACTS improved ESS, HTLC, and LI scores on degraded textures by 40.69%, 11.16%, and 54.76%, respectively. The framework processes 2.5-megapixel texture in approximately 58.8 s on a single consumer-grade GPU. This work provides a scalable and interpretable solution for the automated restoration of defective facade textures, thereby enhancing the visual realism and structural accuracy of existing 3D urban models. Code and data available at https://github.com/CVEO/FACTS.

合写作者：Xiangru Huang,Tong Wang,Jiaqi Wang,Xiaoliang Tan,Aiyi Jiang

论文类型：期刊论文

通讯作者：Guanzhou Chen,Xiaodong Zhang

卷号：71

页面范围：104385

是否译文：否

发表时间：2026-04-01

收录刊物：SCI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S1474034626000777