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公开(公告)号：US11893690B2
公开(公告)日：2024-02-06
申请号：US17949823
申请日：2022-09-21
Applicant: DASSAULT SYSTEMES
Inventor： Serban Alexandru State , Eloi Mehr , Yoan Souty
Abstract: A computer-implemented method for 3D reconstruction including obtaining 2D images and, for each 2D image, camera parameters which define a perspective projection. The 2D images all represent a same real object. The real object is fixed. The method also includes obtaining, for each 2D image, a smooth map. The smooth map has pixel values, and each pixel value represents a measurement of contour presence. The method also includes determining a 3D modeled object that represents the real object. The determining iteratively optimizes energy. The energy rewards, for each smooth map, projections of silhouette vertices of the 3D modeled object having pixel values representing a high measurement of contour presence. This forms an improved solution for 3D reconstruction.
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公开(公告)号：US11790605B2
公开(公告)日：2023-10-17
申请号：US17139121
申请日：2020-12-31
Applicant: DASSAULT SYSTEMES
Inventor： Serban Alexandru State , Eloi Mehr , Yoan Souty
Abstract: A computer-implemented method for 3D reconstruction including obtaining 2D images and, for each 2D image, camera parameters which define a perspective projection. The 2D images all represent a same real object. The real object is fixed. The method also includes obtaining, for each 2D image, a smooth map. The smooth map has pixel values, and each pixel value represents a measurement of contour presence. The method also includes determining a 3D modeled object that represents the real object. The determining iteratively optimizes energy. The energy rewards, for each smooth map, projections of silhouette vertices of the 3D modeled object having pixel values representing a high measurement of contour presence. This forms an improved solution for 3D reconstruction.
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公开(公告)号：US11657195B2
公开(公告)日：2023-05-23
申请号：US17102254
申请日：2020-11-23
Applicant: DASSAULT SYSTEMES
Inventor： Guillaume Randon , Eloi Mehr
CPC classification number: G06F30/23 , G06F30/12 , G06F30/27 , G06N3/08 , G06T17/20 , G06T2200/04 , G06T2207/10028 , G06T2207/20072
Abstract: A method for processing a shape attribute 3D signal including providing a graph having nodes and arcs, each node representing a point of a 3D discrete representation, each arc representing neighboring points of the representation, providing a set of values representing a distribution of the shape attribute, each value being associated to a node and representing the shape attribute at the point represented by the node, minimizing energy on a Markov Random Field on the graph, the energy penalizing, for each arc connecting a first node associated to a first value to a second node associated to a second value, highness of an increasing function of a distance between the first and second value, a distance between a first point, represented by the first node, and a medial geometrical element of the representation, and a distance between a second point, represented by the second node, and the medial geometrical element.
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公开(公告)号：US11562207B2
公开(公告)日：2023-01-24
申请号：US16727092
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr
Abstract: The disclosure notably relates to a computer-implemented method of machine-learning. The method includes obtaining a dataset including 3D modeled objects which each represent a respective mechanical part and further includes providing a set of neural networks. Each neural network has respective weights. Each neural network is configured for inference of 3D modeled objects. The method further includes modifying respective weights of the neural networks by minimizing a loss. For each 3D modeled object, the loss selects a term among a plurality of terms. Each term penalizes a disparity between the 3D modeled object and a respective 3D modeled object inferred by a respective neural network of the set. The selected term is a term among the plurality of terms for which the disparity is the least penalized. This constitutes an improved method of machine-learning with a dataset including 3D modeled objects which each represent a respective mechanical part.
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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：US20200210814A1
公开(公告)日：2020-07-02
申请号：US16727092
申请日：2019-12-26
Applicant: DASSAULT SYSTEMES
Inventor： Eloi Mehr
Abstract: The disclosure notably relates to a computer-implemented method of machine-learning. The method includes obtaining a dataset including 3D modeled objects which each represent a respective mechanical part and further includes providing a set of neural networks. Each neural network has respective weights. Each neural network is configured for inference of 3D modeled objects. The method further includes modifying respective weights of the neural networks by minimizing a loss. For each 3D modeled object, the loss selects a term among a plurality of terms. Each term penalizes a disparity between the 3D modeled object and a respective 3D modeled object inferred by a respective neural network of the set. The selected term is a term among the plurality of terms for which the disparity is the least penalized. This constitutes an improved method of machine-learning with a dataset including 3D modeled objects which each represent a respective mechanical part.
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公开(公告)号：US20180330184A1
公开(公告)日：2018-11-15
申请号：US15975407
申请日：2018-05-09
Applicant: DASSAULT SYSTEMES
Abstract: A computer-implemented method for determining an architectural layout. The method comprises providing a cycle of points that represents a planar cross section of a cycle of walls, and, assigned to each respective point, a respective first datum that represents a direction normal to the cycle of points at the respective point. The method also comprises minimizing a Markov Random Field energy thereby assigning, to each respective point, a respective one of the set of second data. The method also comprises identifying maximal sets of consecutive points to which a same second datum is assigned, and a cycle of vertices bounding a cycle of segments which represents the architectural layout. Such a method constitutes an improved solution for determining an architectural layout.
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公开(公告)号：US11556678B2
公开(公告)日：2023-01-17
申请号：US16723884
申请日：2019-12-20
Applicant: Dassault Systemes
Inventor： Eloi Mehr
Abstract: A computer-implemented method for designing a 3D modeled object via user-interaction. The method includes obtaining the 3D modeled object and a machine-learnt decoder. The machine-learnt decoder is a differentiable function taking values in a latent space and outputting values in a 3D modeled object space. The method further includes defining a deformation constraint for a part of the 3D modeled object. The method further comprises determining an optimal vector. The optimal vector minimizes an energy. The energy explores latent vectors. The energy comprises a term which penalizes, for each explored latent vector, non-respect of the deformation constraint by the result of applying the decoder to the explored latent vector. The method further includes applying the decoder to the optimal latent vector. This constitutes an improved method for designing a 3D modeled object via user-interaction.
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