We present DualCSG, a novel neural network composed of two dual and complementary branches for unsupervised learning of constructive solid geometry (CSG) representations of 3D CAD shapes. Our network is trained to reconstruct a given 3D CAD shape through a compact assembly of quadric surface primitives via fixed-order CSG operations along two branches. The key difference between our method and all previous neural CSG models is that DualCSG has a dedicated branch, the residual branch, to assemble the potentially complex, complement or residual shape that is to be subtracted from an overall cover shape. The cover shape is modeled by the other branch, the cover branch. Both branches construct a union of primitive intersections, where the only difference is that the residual branch also learns primitive inverses while operating in the complement space. With the shape complements, our network is provably general. We demonstrate both quantitatively and qualitatively that our network produces CSG reconstructions with superior quality, more natural trees, and better quality-compactness tradeoff than all existing alternatives, especially over complex and high-genus CAD shapes.

翻译：我们提出DualCSG，一种新颖的神经网络，由两条对偶且互补的分支组成，用于无监督学习三维CAD形状的构造实体几何表示。该网络通过沿两条分支执行固定顺序的CSG操作，以紧凑的二次曲面基元集合重建给定的三维CAD形状。我们方法与此前所有神经CSG模型的关键区别在于，DualCSG拥有一条专用分支（残差分支），用于组装需要从整体覆盖形状中减去的潜在复杂补形状或残差形状。覆盖形状由另一条分支（覆盖分支）建模。两条分支均构建基元交集的并集，唯一区别在于残差分支在补空间中操作时还学习基元的逆操作。借助形状补集，我们的网络具有可证明的通用性。我们通过定量和定性实验证明，与现有所有替代方法相比，尤其是对于复杂和高亏格CAD形状，我们的网络能够生成质量更优、树结构更自然、质量-紧凑性权衡更好的CSG重建结果。