JLGS-CAD: CAD Reconstruction Based on Joint Learning for Geometry and Sequence

Achieving both high accuracy and greater similarity to the modeling process of human engineers in CAD reconstruction tasks is a challenging problem. In this paper, we propose JLGS-CAD, a neural network designed based on joint learning, aiming to coordinatively maximize the accuracy of model reconstruction and the quality of the modeling sequence. Based on the characteristics of the CAD modeling process, we divide the reconstruction task between two models: the Extrusion Model, responsible for the geometric accuracy of the reconstructed shape, and the Sketch Model, responsible for the quality of the modeling sequence. We adopt a hybrid supervision approach to enable joint learning of both sequences and geometry in the two models. This method significantly improves the quality of the modeling sequence while maintaining the precision of the reconstructed geometry, allowing the network to produce results more aligned with human modeling workflows. Our training pipeline consists of two stages: a supervised pre-training stage on a large-scale dataset with sequence annotations and a self-supervised fine-tuning stage on a target dataset without sequence labels. This reduces the network�s dependency on large annotated CAD modeling datasets. Experiments conducted on the ABC and Fusion 360 datasets demonstrate the effectiveness of our method. JLGS-CAD accurately recovers geometric details and constructs editable and creative modeling workflows, showing clear advantages over state-of-the-art alternatives.