Deng Zhi, Liu Yang, Pan Hao, Jabi Wassim, Zhang Juyong, Deng Bailin
IEEE Trans Vis Comput Graph. 2023 Sep;29(9):3826-3839. doi: 10.1109/TVCG.2022.3170853. Epub 2023 Aug 1.
The freeform architectural modeling process often involves two important stages: concept design and digital modeling. In the first stage, architects usually sketch the overall 3D shape and the panel layout on a physical or digital paper briefly. In the second stage, a digital 3D model is created using the sketch as a reference. The digital model needs to incorporate geometric requirements for its components, such as the planarity of panels due to consideration of construction costs, which can make the modeling process more challenging. In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes. Our system allows the user to sketch the surface boundary and contour lines under axonometric projection and supports the sketching of occluded regions. In addition, the user can sketch feature lines to provide directional guidance to the PQ mesh layout. Given the 2D sketch input, we propose a deep neural network to infer in real-time the underlying surface shape along with a dense conjugate direction field, both of which are used to extract the final PQ mesh. To train and validate our network, we generate a large synthetic dataset that mimics architect sketching of freeform quadrilateral patches. The effectiveness and usability of our system are demonstrated with quantitative and qualitative evaluation as well as user studies.
概念设计和数字建模。在第一阶段，建筑师通常在实体或数字图纸上简要勾勒出整体3D形状和面板布局。在第二阶段，以草图为参考创建数字3D模型。由于考虑到建造成本，数字模型需要纳入其组件的几何要求，例如面板的平面度，这会使建模过程更具挑战性。在这项工作中，我们提出了一种新颖的基于草图的系统，以弥合以平面四边形（PQ）网格表示的自由形式屋顶形状的概念设计和数字建模之间的差距。我们的系统允许用户在轴测投影下绘制表面边界和轮廓线，并支持绘制遮挡区域。此外，用户可以绘制特征线，为PQ网格布局提供方向指导。给定2D草图输入，我们提出了一种深度神经网络，以实时推断基础表面形状以及密集共轭方向场，这两者都用于提取最终的PQ网格。为了训练和验证我们的网络，我们生成了一个大型合成数据集，该数据集模仿了自由形式四边形面片的建筑师草图。我们通过定量和定性评估以及用户研究证明了我们系统的有效性和可用性。
