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公开(公告)号：US10783707B2
公开(公告)日：2020-09-22
申请号：US16230841
申请日：2018-12-21
Applicant: Dassault Systemes
Abstract: The disclosure notably relates to a computer-implemented method for 3D reconstruction. The method comprises providing a 3D point cloud representing a real object. The method also comprises fitting the 3D point cloud with parametric surfaces. The method also comprises defining a partition of the parametric surfaces into oriented facets which respect intersections between the parametric surfaces. The method also comprises determining, among the oriented facets of the partition, a set of facets that represents a skin of the real object. The determining comprises minimizing an energy. The energy includes a data term and a constraint term. The data term increasingly penalizes discarding facets, as a level of fit between a discarded facet and the 3D point cloud increases. The constraint term penalizes formation of non-skin geometry by kept facets. Such a method provides an improved solution for 3D reconstruction.
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公开(公告)号：US20200250894A1
公开(公告)日：2020-08-06
申请号：US16727207
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr , Fernando Manuel Sanchez Bermudez
IPC: G06T19/20 , G06N3/08 , G06F30/12 , G06F16/901
Abstract: The disclosure notably relates to a computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree comprises a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining respective data pieces, and inserting a part of the data pieces in the dataset each as a respective training sample. The respective 3D shape of each of one or more first data pieces inserted in the dataset is identical to the respective 3D shape of respective one or more second data pieces not inserted in the dataset. The method forms an improved solution for digitization.
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公开(公告)号：US11210866B2
公开(公告)日：2021-12-28
申请号：US16727207
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr , Fernando Manuel Sanchez Bermudez
IPC: G06T19/20 , G06F16/901 , G06F30/12 , G06N3/08 , G06T17/10 , G06N3/04 , G06N5/00 , G06T17/00 , G06F9/451
Abstract: The disclosure notably relates to a computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree comprises a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining respective data pieces, and inserting a part of the data pieces in the dataset each as a respective training sample. The respective 3D shape of each of one or more first data pieces inserted in the dataset is identical to the respective 3D shape of respective one or more second data pieces not inserted in the dataset. The method forms an improved solution for digitization.
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公开(公告)号：US11100710B2
公开(公告)日：2021-08-24
申请号：US16730833
申请日：2019-12-30
Applicant: DASSAULT SYSTEMES
Abstract: The disclosure notably relates to a computer-implemented method for extracting a feature tree from a mesh. The method includes providing a mesh, computing a geometric and adjacency graph of the provided mesh, wherein each node of the graph represents one region of the mesh and comprises a primitive type and parameters of the region, each connection between two nodes is an intersection between the respective surfaces of the regions represented by the two connected nodes. The method also includes instantiating for each node of the graph, a surface based on the identified primitive type and parameters of the region.
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公开(公告)号：US11922573B2
公开(公告)日：2024-03-05
申请号：US16727413
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Fernando Manuel Sanchez Bermudez , Eloi Mehr
IPC: G06T17/10 , G06F30/23 , G06F30/27 , G06N3/084 , G06N3/088 , G06N5/046 , G06N20/10 , G06T17/20 , G06V10/44 , G06V10/764 , G06V10/82 , G06V20/64 , G06F119/18
CPC classification number: G06T17/10 , G06F30/23 , G06F30/27 , G06N3/084 , G06N3/088 , G06N5/046 , G06N20/10 , G06T17/20 , G06V10/454 , G06V10/764 , G06V10/82 , G06V20/647 , G06F2119/18
Abstract: The disclosure notably relates to computer-implemented method for learning a neural network configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes providing a dataset including freehand drawings each representing a respective 3D shape, and learning the neural network based on the dataset. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.
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Patent Agency Ranking
公开(公告)号：US11514214B2
公开(公告)日：2022-11-29
申请号：US16727338
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Fernando Manuel Sanchez Bermudez , Eloi Mehr
Abstract: A computer-implemented method for forming a dataset configured for learning a neural network. The neural network is configured for inference, from a freehand drawing representing a 3D shape, of a solid CAD feature representing the 3D shape. The method includes generating one or more solid CAD feature includes each representing a respective 3D shape. The method also includes, for each solid CAD feature, determining one or more respective freehand drawings each representing the respective 3D shape, and inserting in the dataset, one or more training samples. Each training sample includes the solid CAD feature and a respective freehand drawing. The method forms an improved solution for inference, from a freehand drawing representing a 3D shape, of a 3D modeled object representing the 3D shape.
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公开(公告)号：US20200211276A1
公开(公告)日：2020-07-02
申请号：US16727124
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr , Fernando Manuel Sanchez Bermudez
Abstract: The disclosure notably relates to a computer-implemented method for learning a neural network configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree includes a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining a dataset including discrete geometrical representations each of a respective 3D shape, and obtaining a candidate set of leaf geometrical shapes. The method also includes learning the neural network based on the dataset and on the candidate set. The candidate set includes at least one continuous subset of leaf geometrical shapes. The method forms an improved solution for digitization.
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公开(公告)号：US11436795B2
公开(公告)日：2022-09-06
申请号：US16727124
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr , Fernando Manuel Sanchez Bermudez
Abstract: The disclosure notably relates to a computer-implemented method for learning a neural network configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree includes a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining a dataset including discrete geometrical representations each of a respective 3D shape, and obtaining a candidate set of leaf geometrical shapes. The method also includes learning the neural network based on the dataset and on the candidate set. The candidate set includes at least one continuous subset of leaf geometrical shapes. The method forms an improved solution for digitization.
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公开(公告)号：US11195330B2
公开(公告)日：2021-12-07
申请号：US16730795
申请日：2019-12-30
Applicant: DASSAULT SYSTEMES
Abstract: The disclosure notably relates to a computer-implemented method for generating a structured three-dimensional (3D) model from a mesh. The method includes obtaining a mesh that comprises faces, each face of the mesh including a normal and principal curvature values; computing a distribution of the principal curvatures values over the whole mesh by counting the number of occurrences of discretized curvature values; identifying in the computed distribution one or more dominant ranges of principal curvature values; for each identified dominant range, computing one or more regions of the mesh that includes faces belonging to the identified dominant range; for each computed region, detecting a primitive type by using the curvatures values of all faces of the region and identifying parameters of the detected primitive by using the mesh surface of the region.
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公开(公告)号：US20200211281A1
公开(公告)日：2020-07-02
申请号：US16730833
申请日：2019-12-30
Applicant: Dassault Systemes
Abstract: The disclosure notably relates to a computer-implemented method for extracting a feature tree from a mesh. The method includes providing a mesh, computing a geometric and adjacency graph of the provided mesh, wherein each node of the graph represents one region of the mesh and comprises a primitive type and parameters of the region, each connection between two nodes is an intersection between the respective surfaces of the regions represented by the two connected nodes. The method also includes instantiating for each node of the graph, a surface based on the identified primitive type and parameters of the region.
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