We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Our network takes an input 3D shape that can be provided as a point cloud or voxel grids, and reconstructs it by a compact assembly of quadric surface primitives via constructive solid geometry (CSG) operations. The network is self-supervised with a reconstruction loss, leading to faithful 3D reconstructions with sharp edges and plausible CSG trees, without any ground-truth shape assemblies. While the parametric nature of CAD models does make them more predictable locally, at the shape level, there is a great deal of structural and topological variations, which present a significant generalizability challenge to state-of-the-art neural models for 3D shapes. Our network addresses this challenge by adaptive training with respect to each test shape, with which we fine-tune the network that was pre-trained on a model collection. We evaluate our learning framework on both ShapeNet and ABC, the largest and most diverse CAD dataset to date, in terms of reconstruction quality, shape edges, compactness, and interpretability, to demonstrate superiority over current alternatives suitable for neural CAD reconstruction.

中文翻译：

---

CAPRI-Net：使用自适应基元装配学习紧凑型CAD形状

我们引入了CAPRI-Net，这是一种用于学习3D计算机辅助设计（CAD）模型的紧凑且可解释的隐式表示的神经网络，形式为自适应图元程序集。我们的网络采用可作为点云或体素网格提供的输入3D形状，并通过构造性实体几何（CSG）操作通过二次曲面图元的紧凑组装来重构它。该网络是自我监督的，且存在重建损失，从而导致了忠实的3D重建，具有清晰的边缘和合理的CSG树，而没有任何地面真实形状的装配。虽然CAD模型的参数性质确实使它们在局部更易于预测，但在形状级别上却存在大量结构和拓扑变化，这对3D形状的最新神经模型提出了重大的通用性挑战。我们的网络通过针对每种测试形状的自适应训练来应对这一挑战，通过这种训练，我们可以对在模型集合上进行预训练的网络进行微调。我们在重构质量，形状边缘，紧凑性和可解释性方面评估了ShapeNet和ABC（迄今为止最大和最多样化的CAD数据集）上的学习框架，以证明其优于当前适用于神经CAD重构的替代方法的优越性。