The accurate representation of 3D building models in urban environments is significantly hindered by challenges such as texture occlusion, blurring, and missing details, which are difficult to mitigate through standard photogrammetric texture mapping pipelines. Current image completion methods often struggle to produce structured results and effectively handle the intricate nature of highly-structured fa\c{c}ade textures with diverse architectural styles. Furthermore, existing image synthesis methods encounter difficulties in preserving high-frequency details and artificial regular structures, which are essential for achieving realistic fa\c{c}ade texture synthesis. To address these challenges, we introduce a novel approach for synthesizing fa\c{c}ade texture images that authentically reflect the architectural style from a structured label map, guided by a ground-truth fa\c{c}ade image. In order to preserve fine details and regular structures, we propose a regularity-aware multi-domain method that capitalizes on frequency information and corner maps. We also incorporate SEAN blocks into our generator to enable versatile style transfer. To generate plausible structured images without undesirable regions, we employ image completion techniques to remove occlusions according to semantics prior to image inference. Our proposed method is also capable of synthesizing texture images with specific styles for fa\c{c}ades that lack pre-existing textures, using manually annotated labels. Experimental results on publicly available fa\c{c}ade image and 3D model datasets demonstrate that our method yields superior results and effectively addresses issues associated with flawed textures. The code and datasets will be made publicly available for further research and development.

翻译：城市环境中3D建筑模型的精确表示受到纹理遮挡、模糊和细节缺失等问题的严重制约，这些问题难以通过标准的摄影测量纹理映射流程加以缓解。现有的图像补全方法通常难以产生结构化结果，也无法有效处理具有多样化建筑风格的高度结构化立面纹理的复杂特性。此外，现有的图像合成方法在保留高频细节和人工规则结构方面存在困难，而这些对于实现逼真的立面纹理合成至关重要。为应对这些挑战，本文提出一种新颖方法，用于合成能够真实反映建筑风格的结构化标签图对应的立面纹理图像，并以真实立面图像为指导。为了保留精细细节和规则结构，我们提出了一种基于规则感知的多域方法，该方法充分利用频率信息和角点图。同时，我们在生成器中引入SEAN模块以实现灵活的样式迁移。为了生成合理的结构化图像并避免出现不良区域，我们在图像推理前采用图像补全技术，根据语义先验移除遮挡。所提出的方法还能利用人工标注的标签，为缺乏预设纹理的立面合成特定风格的纹理图像。在公开的立面图像和3D模型数据集上的实验结果表明，我们的方法能产生更优的结果，并有效解决与纹理缺陷相关的问题。代码和数据集将公开发布，以促进进一步的研究与开发。