eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural Networks

• Published in: Computer aided design Vol. 178; p. 103806
• Main Authors: Zhang, Chao, Polette, Arnaud, Pinquié, Romain, Carasi, Gregorio, De Charnace, Henri, Pernot, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025; Elsevier
• Subjects: CAD model reconstruction; Computer Aided Engineering; Computer Science; Deep neural networks; Editable CAD models; Parametric modeling; Deep neural networks; Parametric modeling; Editable CAD models; CAD model reconstruction
• ISSN: 0010-4485
• DOI: 10.1016/j.cad.2024.103806

This paper introduces a novel framework capable of reconstructing editable parametric CAD models from dumb B-Rep models. First, each B-Rep model is represented with a network-friendly formalism based on UV-graph, which is then used as input of eCAD-Net, the new deep neural network-based algorithm that predicts feature-based CAD modeling sequences from the graph. Then, the sequences are scaled and fine-tuned using a feature matching algorithm that retrieves the exact parameter values from the input dumb CAD model. The output sequences are then converted in a series of CAD modeling operations to create an editable parametric CAD model in any CAD modeler. A cleaned dataset is used to learn and validate the proposed approach, and is provided with the article. The experimental results show that our approach outperforms existing methods on such reconstruction tasks, and it outputs editable parametric CAD models compatible with existing CAD modelers and ready for use in downstream engineering applications. •A novel network to infer sequences of sketch-extrude operations from B-Rep models.•A feature-matching algorithm to fine-tune the sequences predicted by the networks.•A new algorithm to clean the dataset used for training and validation.