Computer Aided Design (CAD), especially the feature-based parametric CAD, plays an important role in modern industry and society. However, the reconstruction of featured CAD model is more challenging than the reconstruction of other CAD models. To this end, this paper proposes an end-to-end network to reconstruct featured CAD model from point cloud (P2CADNet). Initially, the proposed P2CADNet architecture combines a point cloud feature extractor, a CAD sequence reconstructor and a parameter optimizer. Subsequently, in order to reconstruct the featured CAD model in an autoregressive way, the CAD sequence reconstructor applies two transformer decoders, one with target mask and the other without mask. Finally, for predicting parameters more precisely, we design a parameter optimizer with cross-attention mechanism to further refine the CAD feature parameters. We evaluate P2CADNet on the public dataset, and the experimental results show that P2CADNet has excellent reconstruction quality and accuracy. To our best knowledge, P2CADNet is the first end-to-end network to reconstruct featured CAD model from point cloud, and can be regarded as baseline for future works. Therefore, we open the source code at https://github.com/Blice0415/P2CADNet.

翻译：计算机辅助设计（CAD），特别是基于特征的参数化CAD，在现代工业与社会中扮演着重要角色。然而，相较于其他CAD模型的重建，特征化CAD模型的重建更具挑战性。为此，本文提出了一种从点云重建特征化CAD模型的端到端网络（P2CADNet）。首先，所提出的P2CADNet架构结合了点云特征提取器、CAD序列重建器以及参数优化器。其次，为了以自回归方式重建特征化CAD模型，CAD序列重建器应用了两个Transformer解码器，一个带有目标掩码，另一个不带掩码。最后，为更精确地预测参数，我们设计了一个具有交叉注意力机制的参数优化器，以进一步细化CAD特征参数。我们在公开数据集上评估了P2CADNet，实验结果表明，P2CADNet具有优秀的重建质量与精度。据我们所知，P2CADNet是首个从点云重建特征化CAD模型的端到端网络，可作为未来工作的基线。因此，我们在https://github.com/Blice0415/P2CADNet 开源了源代码。