公开(公告)号：US20170178400A1

公开(公告)日：2017-06-22

申请号：US15387189

申请日：2016-12-21

Applicant: DASSAULT SYSTEMES

Inventor： Malika BOULKENAFED ,  Jean Julien TUFFREAU

IPC: G06T17/00 ,  G06T1/60 ,  H04N19/426 ,  G06T15/50 ,  G06T19/20 ,  G06K9/62

CPC classification number: G06T17/00 ,  G06K9/6201 ,  G06T1/60 ,  G06T15/50 ,  G06T19/20 ,  G06T2200/16 ,  H04N19/426

Abstract: The invention notably relates to a computer-implemented method for displaying a 3D assembly of modeled objects. The method comprises streaming from a first computer to a second computer at least one raster image of a first 3D modeled object, and rendering on the second computer the 3D assembly of modeled objects by merging a second 3D modeled object with the streamed at least one raster image.

公开(公告)号：US20220101105A1

公开(公告)日：2022-03-31

申请号：US17486684

申请日：2021-09-27

Applicant: DASSAULT SYSTEMES ,  ECOLE POLYTECHNIQUE ,  CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE

Inventor： Mariem MEZGHANNI ,  Maks OVSJANIKOV ,  Malika BOULKENAFED

IPC: G06N3/04 ,  G06N3/08

Abstract: A computer-implemented method for training a deep-learning generative model configured to output 3D modeled objects each representing a mechanical part or an assembly of mechanical parts. The method comprises obtaining a dataset of 3D modeled objects and training the deep-learning generative model based on the dataset. The training includes minimization of a loss. The loss includes a term that penalizes, for each output respective 3D modeled object, one or more functional scores of the respective 3D modeled object. Each functional score measures an extent of non-respect of a respective functional descriptor among one or more functional descriptors, by the mechanical part or the assembly of mechanical parts. This forms an improved solution with respect to outputting 3D modeled objects each representing a mechanical part or an assembly of mechanical parts.

公开(公告)号：US20180096514A1

公开(公告)日：2018-04-05

申请号：US15719267

申请日：2017-09-28

Applicant: DASSAULT SYSTEMES

Inventor： Malika BOULKENAFED ,  Philippe Robert Felix BELMANS

IPC: G06T15/00 ,  G06F17/50

CPC classification number: G06T15/005 ,  G06F9/5083 ,  G06F17/50 ,  G06T7/10 ,  G06T17/00 ,  G06T2200/04 ,  G06T2200/16 ,  G06T2210/52

Abstract: The invention notably relates to a computer-implemented method for simulating a 3D scene. The simulation is carried out with a set of computing resources running in parallel. The method comprises partitioning a 3D scene into a plurality of zones. Each zone is sized to satisfy real-time computing constraint by one computing resource of the set. The method comprises assigning each zone of the plurality to a computing resource, computing an estimation of a load of each computing resource and determining whether one or more computing resources are over-loaded or under-loaded, computing, for each zone, a contribution of the zone to the load of the computing resource to which the zone is assigned, reassigning one or more zones of a computing resource that is over-loaded or under-loaded to another computing resource, the reassignment resulting from the computed contributions of the zones with a combinatorial optimization algorithm.

公开(公告)号：US20240251103A1

公开(公告)日：2024-07-25

申请号：US18413163

申请日：2024-01-16

Applicant: DASSAULT SYSTEMES ,  ECOLE POLYTECHNIQUE ,  CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE

Inventor： Mariem MEZGHANNI ,  Kawtar ZAHER ,  Malika BOULKENAFED ,  Maks OVSJANIKOV

IPC: H04N19/597 ,  G06T17/00 ,  H04N19/94

CPC classification number: H04N19/597 ,  G06T17/00 ,  H04N19/94

Abstract: A computer-implemented method of machine-learning. The method includes obtaining a training dataset of 3D models of real-world objects. The method further includes learning, based on the training dataset and on a patch-decomposition of the 3D models of the training dataset, a finite codebook of quantized vectors and a neural network. The neural network comprises a rotation-invariant encoder. The rotation-invariant encoder is configured for rotation-invariant encoding of a patch of a 3D model into a quantized latent vector of the codebook. The neural network further includes a decoder. The decoder is configured for decoding a sequence of quantized latent vectors of the codebook into a 3D model. The sequence corresponds to a patch-decomposition. This constitutes an improved solution for 3D model generation.

公开(公告)号：US20220405448A1

公开(公告)日：2022-12-22

申请号：US17829987

申请日：2022-06-01

Applicant: DASSAULT SYSTEMES ,  ECOLE POLYTECHNIQUE ,  CNRS

Inventor： Mariem MEZGHANNI ,  Théo BODRITO ,  Malika BOULKENAFED ,  Maks OVSJANIKOV

IPC: G06F30/27 ,  G06F30/17 ,  G06N3/08

Abstract: A computer-implemented method of machine-learning. The method comprises providing a dataset of 3D modeled objects each representing a mechanical part. Each 3D modeled object comprises a specification of a geometry of the mechanical part. The method further comprises learning a set of parameterization vectors each respective to a respective 3D modeled object of the dataset and a neural network configured to take as input a parameterization vector and to output a representation of a 3D modeled object usable in a differentiable simulation-based shape optimization. The learning comprises minimizing a loss that penalizes, for each 3D modeled object of the dataset, a disparity between the output of the neural network for an input parameterization vector respective to the 3D modeled object and a representation of the 3D modeled object. The representation of the 3D modeled object is usable in a differentiable simulation-based shape optimization.

公开(公告)号：US20200218838A1

公开(公告)日：2020-07-09

申请号：US16824372

申请日：2020-03-19

Applicant: DASSAULT SYSTEMES

Inventor： Malika BOULKENAFED ,  Philippe Robert Felix BELMANS

IPC: G06F30/20 ,  G06F30/13 ,  G06T17/00 ,  G06T15/00 ,  A63F13/00 ,  G06T19/20 ,  G06T7/11

Abstract: Described is a computer-implemented method for partitioning a 3D scene into a plurality of zones, each zone representing an area or a volume of the 3D scene and being processed by a computing resource. The method comprises obtaining a 3D scene comprising one or more objects, each object generating a computing resource cost, computing a first map that represents a density of computing costs of the provided 3D scene, defining a second map that represents constraints on the shapes of zones that will be obtained as a result of a partitioning of the 3D scene, discretizing the obtained 3D scene into cells by computing a space quantization of the 3D scene free of dynamic objects, computing, for each cell, a computing cost from the first map of the 3D scene, aggregating the cells into one or more zones in accordance with the second map.

公开(公告)号：US20190243928A1

公开(公告)日：2019-08-08

申请号：US16235930

申请日：2018-12-28

Applicant: DASSAULT SYSTEMES

Inventor： Asma REJEB SFAR ,  Louis DUPONT DE DINECHIN ,  Malika BOULKENAFED

IPC: G06F17/50 ,  G06N3/08 ,  G06K9/62

CPC classification number: G06F17/5004 ,  G06K9/00476 ,  G06K9/342 ,  G06K9/4628 ,  G06K9/6256 ,  G06K9/627 ,  G06N3/08

Abstract: The disclosure notably relates to a computer-implemented method for determining a function configured to determine a semantic segmentation of a 2D floor plan representing a layout of a building. The method comprises providing a dataset comprising 2D floor plans each associated to a respective semantic segmentation. The method also comprises learning the function based on the dataset. Such a method provides an improved solution for processing a 2D floor plan.

公开(公告)号：US20210192254A1

公开(公告)日：2021-06-24

申请号：US17124452

申请日：2020-12-16

Applicant: DASSAULT SYSTEMES

Inventor： Asma REJEB SFAR ,  Tom DURAND ,  Malika BOULKENAFED

IPC: G06K9/20 ,  G06T7/11 ,  G06N3/08

Abstract: A computer-implemented method of machine-learning including obtaining a dataset of 3D point clouds. Each 3D point cloud includes at least one object. Each 3D point cloud is equipped with a specification of one or more graphical user-interactions each representing a respective selection operation of a same object in the 3D point cloud. The method further includes teaching, based on the dataset, a neural network configured for segmenting an input 3D point cloud including an object. The segmenting is based on the input 3D point cloud and on a specification of one or more input graphical user-interactions each representing a respective selection operation of the object in the 3D point cloud.

公开(公告)号：US20180329892A1

公开(公告)日：2018-11-15

申请号：US15969548

申请日：2018-05-02

Applicant: DASSAULT SYSTEMES

Inventor： Niels LUBBERS ,  Malika BOULKENAFED

IPC: G06F17/28 ,  G06N3/08 ,  G06K9/62

Abstract: A computer implemented method for learning a function configured for captioning a region of an image. The method comprises providing a dataset of triplets each including a respective image, a respective region of the respective image, and a respective caption of the respective region. The method also comprises learning, with the dataset of triplets, a function that is configured to generate an output caption based on an input image and on an input region of the input image. Such a method constitutes an improved solution for captioning a region of an image.

公开(公告)号：US20170169544A1

公开(公告)日：2017-06-15

申请号：US15370326

申请日：2016-12-06

Applicant: DASSAULT SYSTEMES

Inventor： Malika BOULKENAFED ,  Jean-Julien Tuffreau

IPC: G06T3/40 ,  G06T1/60

Abstract: The invention notably relates to a memory storage having a linear track and having recorded thereon a multi-resolution image system of an object, the multi-resolution image system including a set of images, each image representing the object and having a respective resolution, wherein the recording is according to a continuous injection from a space-filling curve of the set of images to the linear track, the space-filling curve interlaces the different images, and the intersection between the space-filling curve and each image is on a Hilbert curve.The invention improves the way to record a multi-resolution image system of an object on a memory storage.