Abstract
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECAD-Net, an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models in a self-supervised manner. Drawing inspiration from the modeling language that is most commonly used in modern CAD software, we propose to learn 2D sketches and 3D extrusion parameters from raw shapes, from which a set of extrusion cylinders can be generated by extruding each sketch from a 2D plane into a 3D body. By incorporating the Boolean operation (i.e., union), these cylinders can be combined to closely approximate the target geometry. We advocate the use of implicit fields for sketch representation, which allows for creating CAD variations by interpolating latent codes in the sketch latent space. Extensive experiments on both ABC and Fusion 360 datasets demonstrate the effectiveness of our method, and show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction. We further apply our approach to CAD editing and single-view CAD reconstruction. The code is released at this https URL.
Abstract (translated)
从 raw 几何逆向工程 CAD 模型是经典的但艰苦的研究问题。由于监督设计模式或重构不易修改的 CAD 形状,过去的基于学习的方法在很大程度上依赖于标签。在本研究中,我们介绍了SECAD-Net,一个端到端的神经网络,旨在以自我监督的方式重构紧凑且易于编辑的 CAD 模型。从现代 CAD 软件中最常用的建模语言中汲取灵感,我们提议从 raw 形状中学习 2D Sketch 和 3D 挤出参数,从这些参数中可以生成一组挤出圆柱形,这些圆柱形可以通过将每个 Sketch 从 2D 平面挤出到 3D 身体生成。通过加入布尔运算(即合并),这些圆柱形可以组合起来,几乎接近目标几何形状。我们倡导使用隐含 fields 用于 Sketch 表示,这可以通过在 Sketch 隐示空间中插值预填充代码来创建 CAD 变异。在 ABC 和 Fusion 360 数据集上进行广泛的实验证明了我们方法的有效性,并显示了比当前先进技术包括监督 CAD 重构方法的优越性。我们还将其方法应用于 CAD 编辑和单视图 CAD 重构。代码在此 https URL 上发布。
URL
https://arxiv.org/abs/2303.10613
