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

Article dans une revue avec comité de lecture
Author
ZHANG, Chao
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
PINQUIE, Romain
1043329 Institut polytechnique de Grenoble - Grenoble Institute of Technology [Grenoble INP]
CARASI, Gregorio
DE CHARNACE, Henri
ccPERNOT, Jean-Philippe
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/26196
DOI
10.1016/j.cad.2024.103806
Date
2025-01
Journal
Computer-Aided Design

Abstract

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 UVgraph, 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

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