The lack of fa\c{c}ade structures in photogrammetric mesh models renders them inadequate for meeting the demands of intricate applications. Moreover, these mesh models exhibit irregular surfaces with considerable geometric noise and texture quality imperfections, making the restoration of structures challenging. To address these shortcomings, we present StructuredMesh, a novel approach for reconstructing fa\c{c}ade structures conforming to the regularity of buildings within photogrammetric mesh models. Our method involves capturing multi-view color and depth images of the building model using a virtual camera and employing a deep learning object detection pipeline to semi-automatically extract the bounding boxes of fa\c{c}ade components such as windows, doors, and balconies from the color image. We then utilize the depth image to remap these boxes into 3D space, generating an initial fa\c{c}ade layout. Leveraging architectural knowledge, we apply binary integer programming (BIP) to optimize the 3D layout's structure, encompassing the positions, orientations, and sizes of all components. The refined layout subsequently informs fa\c{c}ade modeling through instance replacement. We conducted experiments utilizing building mesh models from three distinct datasets, demonstrating the adaptability, robustness, and noise resistance of our proposed methodology. Furthermore, our 3D layout evaluation metrics reveal that the optimized layout enhances precision, recall, and F-score by 6.5%, 4.5%, and 5.5%, respectively, in comparison to the initial layout.

翻译：摄影测量网格模型缺少立面结构，导致其无法满足复杂应用的需求。此外，此类网格模型表面不规则，存在显著几何噪声和纹理质量缺陷，使得结构恢复极具挑战性。针对上述问题，本文提出StructuredMesh——一种在摄影测量网格模型中重建符合建筑规律性的立面结构的新方法。该方法通过虚拟相机捕获建筑模型的多视角彩色图像与深度图像，并采用深度学习目标检测管线从彩色图像中半自动提取窗户、门、阳台等立面组件的边界框。随后利用深度图像将这些边界框重新映射到三维空间，生成初始立面布局。基于建筑学知识，我们应用二值整数规划（BIP）优化三维布局结构，涵盖所有组件的位置、朝向及尺寸。优化后的布局进一步通过实例替换驱动立面建模。我们利用来自三个不同数据集的建筑网格模型进行实验，验证了所提方法在适应性、鲁棒性及抗噪性方面的优势。此外，三维布局评估指标表明：相较于初始布局，优化后的布局在精确率、召回率和F-score上分别提升了6.5%、4.5%和5.5%。