https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Fri, 05 Jun 2026 23:00:11 GMT 2026-06-05T23:00:11Z http://hdl.handle.net/10985/26196 eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural Networks ZHANG, Chao; PINQUIE, Romain; CARASI, Gregorio; DE CHARNACE, Henri; PERNOT, Jean-Philippe 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 Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10985/26196 2025-01-01T00:00:00Z ZHANG, Chao PINQUIE, Romain CARASI, Gregorio DE CHARNACE, Henri PERNOT, Jean-Philippe 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 http://hdl.handle.net/10985/26191 Automatic 3D CAD models reconstruction from 2D orthographic drawings ZHANG, Chao; POLETTE, Arnaud; CARASI, Gregorio; DE CHARNACE, Henri; PERNOT, Jean-Philippe This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score. This work was supported by the China Scholarship Council (No. 202108330098) and Cognitive Design Systems. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10985/26191 2023-01-01T00:00:00Z ZHANG, Chao POLETTE, Arnaud CARASI, Gregorio DE CHARNACE, Henri PERNOT, Jean-Philippe This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score.