用于修复建筑模型纹理缺陷的语义图像翻译
Semantic Image Translation for Repairing the Texture Defects of Building Models
AI总结：
提出一种规律感知的多域语义图像翻译方法，结合频率信息、角点图与SEAN块，修复建筑模型纹理缺陷并合成结构化立面纹理，实验证明其优于现有方法。
摘要：
城市环境中3D建筑模型的准确表示受到纹理遮挡、模糊和细节缺失等挑战的严重阻碍，这些挑战很难通过标准摄影测量纹理映射流水线来缓解。当前的图像补全方法通常难以产生结构化结果，并有效处理具有多样建筑风格的高度结构化立面纹理的复杂特性。此外，现有图像合成方法在保留高频细节和人工规则结构方面遇到困难，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，我们引入一种新方法，用于合成真实反映建筑风格的立面纹理图像，该方法基于真实立面图像引导，从结构化标签图生成。为保留精细细节和规则结构，我们提出一种规律感知的多域方法，利用频率信息和角点图。我们还将SEAN块整合到生成器中，以实现灵活的风格迁移。为生成无不良区域的合理结构化图像，我们采用图像补全技术，在图像推断前根据语义去除遮挡。所提方法还能使用手动标注的标签，为缺乏预存纹理的立面合成特定风格的纹理图像。在公开可用的立面图像和3D模型数据集上的实验结果表明，我们的方法产生优越结果，并有效解决与缺陷纹理相关的问题。代码和数据集将公开提供，以供进一步研究和开发。
英文摘要：
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 façade 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 façade texture synthesis. To address these challenges, we introduce a novel approach for synthesizing façade texture images that authentically reflect the architectural style from a structured label map, guided by a ground-truth façade 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 façades that lack pre-existing textures, using manually annotated labels. Experimental results on publicly available façade 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.
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