论文标题
神经半空间表示歧管B-REP固体的隐式转换
Implicit Conversion of Manifold B-Rep Solids by Neural Halfspace Representation
论文作者
论文摘要
我们提出了一种新颖的隐式表示 - 神经半空间表示（NH-REP），以将歧管B-REP固体转换为隐式表示。 NH-REP是一棵布尔树，建立在由神经网络代表的一组隐式函数上，复合布尔函数能够代表固体几何形状，同时保留尖锐的特征。我们提出了一种有效的算法，以从歧管B-Rep固体中提取布尔树，并设计一种基于神经网络的优化方法来计算隐式函数。我们证明了我们的转化算法在一万个歧管B-REP CAD模型上提供的高质量，这些模型包含包括NURB在内的各种曲面，以及我们学习方法优于其他代表性的隐性转换算法在表面重建，尖锐的特征保存，签名距离近距离近似以及对各种表面几何形式的稳健性和应用程序的稳健性和稳健性。
We present a novel implicit representation -- neural halfspace representation (NH-Rep), to convert manifold B-Rep solids to implicit representations. NH-Rep is a Boolean tree built on a set of implicit functions represented by the neural network, and the composite Boolean function is capable of representing solid geometry while preserving sharp features. We propose an efficient algorithm to extract the Boolean tree from a manifold B-Rep solid and devise a neural network-based optimization approach to compute the implicit functions. We demonstrate the high quality offered by our conversion algorithm on ten thousand manifold B-Rep CAD models that contain various curved patches including NURBS, and the superiority of our learning approach over other representative implicit conversion algorithms in terms of surface reconstruction, sharp feature preservation, signed distance field approximation, and robustness to various surface geometry, as well as a set of applications supported by NH-Rep.
