We propose a novel and flexible roof modeling approach that can be used for constructing planar 3D polygon roof meshes. Our method uses a graph structure to encode roof topology and enforces the roof validity by optimizing a simple but effective planarity metric we propose. This approach is significantly more efficient than using general purpose 3D modeling tools such as 3ds Max or SketchUp, and more powerful and expressive than specialized tools such as the straight skeleton. Our optimization-based formulation is also flexible and can accommodate different styles and user preferences for roof modeling. We showcase two applications. The first application is an interactive roof editing framework that can be used for roof design or roof reconstruction from aerial images. We highlight the efficiency and generality of our approach by constructing a mesh-image paired dataset consisting of 2539 roofs. Our second application is a generative model to synthesize new roof meshes from scratch. We use our novel dataset to combine machine learning and our roof optimization techniques, by using transformers and graph convolutional networks to model roof topology, and our roof optimization methods to enforce the planarity constraint.
翻译:我们建议了一种新颖和灵活的屋顶建模方法,可用于建造平板 3D 多边形屋顶板块。 我们的方法使用一个图形结构来编码屋顶的地形, 并通过优化我们提议的简单而有效的平准度度来强制执行屋顶的有效性。 这个方法比使用通用的 3D 建模工具(如 3ds Max 或 StrachUp ) 效率高得多, 比使用普通的 3D 建模工具( 如 3ds Max 或 ScletchUp ) 以及比专用工具( 如 直骨架 ) 更强大、 更能表达的3D 。 我们的优化配制配制也比较灵活, 并且可以适应屋顶建模的不同风格和用户偏好。 我们展示了两个应用程序。 第一个应用程序是互动的屋顶编辑框架, 用于屋顶设计或从空中图像中重建屋顶。 我们强调我们的方法的效率和一般性, 方法是建立一个由2 539 个屋顶配对的数据集。 我们的第二个应用是从头合成合成新式的基因模型模型模型集, 将机器学习和屋顶优化技术结合起来,, 我们使用变换变器和结构网络来模拟屋顶优化方法, 以模拟屋顶修整制成屋顶结构。