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Computer Aided Design (CAD), especially the feature-based parametric CAD,plays an important role in modern industry and society. However, thereconstruction of featured CAD model is more challenging than thereconstruction of other CAD models. To this end, this paper proposes anend-to-end network to reconstruct featured CAD model from point cloud(P2CADNet). Initially, the proposed P2CADNet architecture combines a pointcloud feature extractor, a CAD sequence reconstructor and a parameteroptimizer. Subsequently, in order to reconstruct the featured CAD model in anautoregressive way, the CAD sequence reconstructor applies two transformerdecoders, one with target mask and the other without mask. Finally, forpredicting parameters more precisely, we design a parameter optimizer withcross-attention mechanism to further refine the CAD feature parameters. Weevaluate P2CADNet on the public dataset, and the experimental results show thatP2CADNet has excellent reconstruction quality and accuracy. To our bestknowledge, P2CADNet is the first end-to-end network to reconstruct featured CADmodel from point cloud, and can be regarded as baseline for future works.Therefore, we open the source code at https://github.com/Blice0415/P2CADNet.

P2CADNet: An End-to-End Reconstruction Network for Parametric 3D CAD Model from Point Clouds