公开(公告)号：US11869147B2

公开(公告)日：2024-01-09

申请号：US17408319

申请日：2021-08-20

Applicant: DASSAULT SYSTEMES

Inventor： Nicolas Beltrand

IPC: G06T17/10 ,  G06N3/044

CPC classification number: G06T17/10 ,  G06N3/044 ,  G06T2200/24

Abstract: A computer-implemented method of machine-learning including obtaining an architecture for a neural network which is configured to take as an input a 2D sketch, and to output a 3D model represented by the 2D sketch. The 3D model is a parameterized 3D model defined by a set of parameters consisting of a first subset of one or more parameters and a second subset of one or more parameters. The neural network is configured to selectively output a value for the set and take as input a value for the first subset from a user and output a value for the second subset. The method of machine-learning also includes teaching the neural network.

公开(公告)号：US12002157B2

公开(公告)日：2024-06-04

申请号：US17408258

申请日：2021-08-20

Applicant: DASSAULT SYSTEMES

Inventor： Nicolas Beltrand

IPC: G06T17/10 ,  G06F30/12 ,  G06N3/04

CPC classification number: G06T17/10 ,  G06F30/12 ,  G06N3/04

Abstract: A computer-implemented method of machine-learning. The method includes obtaining a Variational Auto-encoder architecture for a neural network which is configured to take as an input a 2D sketch and a random variable, and to output a 3D model represented by the 2D sketch. The method of machine-learning also includes teaching the neural network.

公开(公告)号：US12190524B2

公开(公告)日：2025-01-07

申请号：US17384299

申请日：2021-07-23

Applicant: DASSAULT SYSTEMES

Inventor： Nicolas Beltrand ,  Mourad Boufarguine ,  Vincent Guitteny

IPC: G06T7/13 ,  G06N3/08 ,  G06T7/143 ,  G06T7/162 ,  G06T7/80 ,  G06T11/20

Abstract: A computer-implemented method for segmenting an object in at least one image acquired by a camera including computing an edge probabilities image based on the image, said edge probabilities image comprising, for each pixel of the image, the probability that said pixel is an edge, computing a segmentation probabilities image based on the image (IM), said segmentation probabilities image comprising, for each pixel of the image (IM), the probability that said pixel belongs to the object (OBJ), and computing a binary mask of the object based on the edge probabilities image and based on the segmentation probabilities image.