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The lack of fa\c{c}ade structures in photogrammetric mesh models renders theminadequate for meeting the demands of intricate applications. Moreover, thesemesh models exhibit irregular surfaces with considerable geometric noise andtexture quality imperfections, making the restoration of structureschallenging. To address these shortcomings, we present StructuredMesh, a novelapproach for reconstructing fa\c{c}ade structures conforming to the regularityof buildings within photogrammetric mesh models. Our method involves capturingmulti-view color and depth images of the building model using a virtual cameraand employing a deep learning object detection pipeline to semi-automaticallyextract the bounding boxes of fa\c{c}ade components such as windows, doors, andbalconies from the color image. We then utilize the depth image to remap theseboxes into 3D space, generating an initial fa\c{c}ade layout. Leveragingarchitectural knowledge, we apply binary integer programming (BIP) to optimizethe 3D layout's structure, encompassing the positions, orientations, and sizesof all components. The refined layout subsequently informs fa\c{c}ade modelingthrough instance replacement. We conducted experiments utilizing building meshmodels from three distinct datasets, demonstrating the adaptability,robustness, and noise resistance of our proposed methodology. Furthermore, our3D layout evaluation metrics reveal that the optimized layout enhancesprecision, recall, and F-score by 6.5%, 4.5%, and 5.5%, respectively, incomparison to the initial layout.

StructuredMesh: 3D Structured Optimization of Fa\c{c}ade Components on Photogrammetric Mesh Models using Binary Integer Programming