DOI number:10.1016/j.aei.2026.104385

Journal:Advanced Engineering Informatics

Abstract: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.

Co-author:Xiangru Huang,Tong Wang,Jiaqi Wang,Xiaoliang Tan,Aiyi Jiang

Indexed by:Journal paper

Correspondence Author:Guanzhou Chen,Xiaodong Zhang

Volume:71

Page Number:104385

Translation or Not:no

Date of Publication:2026-04-01

Included Journals:SCI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S1474034626000777