Patents by Inventor ASMA REJEB SFAR
ASMA REJEB SFAR has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11893313Abstract: 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.Type: GrantFiled: December 16, 2020Date of Patent: February 6, 2024Assignee: DASSAULT SYSTEMESInventors: Asma Rejeb Sfar, Tom Durand, Malika Boulkenafed
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Publication number: 20240028784Abstract: A computer-implemented method for segmenting a building scene including obtaining a training dataset of top-down depth maps. Each depth map includes labeled line segments and junctions between line segments. The method further includes learning, based on the training dataset, a neural network. The neural network is configured to take as input a top-down depth map of a building scene comprising building partitions and to output a scene wireframe including the partitions and junctions between the partitions. This constitutes an improved solution for scene segmentation.Type: ApplicationFiled: July 19, 2023Publication date: January 25, 2024Applicant: DASSAULT SYSTEMESInventors: Asma REJEB SFAR, Markus CHARDONNET
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Patent number: 11763550Abstract: A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.Type: GrantFiled: October 30, 2020Date of Patent: September 19, 2023Assignee: DASSAULT SYSTEMESInventors: Louis Dupont De Dinechin, Asma Rejeb Sfar
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Patent number: 11636234Abstract: The disclosure notably relates to a computer-implemented method for generating a 3D model representing a building. The method comprises providing a 2D floor plan representing a layout of the building. The method also comprises determining a semantic segmentation of the 2D floor plan. The method also comprises determining the 3D model based on the semantic segmentation. Such a method provides an improved solution for processing a 2D floor plan.Type: GrantFiled: December 28, 2018Date of Patent: April 25, 2023Assignee: DASSAULT SYSTEMESInventors: Asma Rejeb Sfar, Louis Dupont de Dinechin, Malika Boulkenafed
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Patent number: 11568109Abstract: A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.Type: GrantFiled: May 6, 2020Date of Patent: January 31, 2023Assignee: DASSAULT SYSTEMESInventors: Asma Rejeb Sfar, Mariem Mezghanni, Malika Boulkenafed
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Publication number: 20220189070Abstract: A computer-implemented method of machine-learning for learning a neural network that encodes a super-point of a 3D point cloud into a latent vector. The method including obtaining a dataset of super-points. Each super-point is a set of points of a 3D point cloud. The set of points represents at least a part of an object. The method further includes learning the neural network based on the dataset of super-points. The learning includes minimizing a loss. The loss penalizes a disparity between two super-points. This constitutes improved machine-learning for 3D object detection.Type: ApplicationFiled: December 16, 2021Publication date: June 16, 2022Applicant: Dassault SystemesInventors: Asma REJEB SFAR, Tom DURAND, Ashad HOSENBOCUS
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Publication number: 20210192254Abstract: 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.Type: ApplicationFiled: December 16, 2020Publication date: June 24, 2021Applicant: DASSAULT SYSTEMESInventors: Asma REJEB SFAR, Tom DURAND, Malika BOULKENAFED
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Patent number: 10929721Abstract: A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.Type: GrantFiled: May 7, 2018Date of Patent: February 23, 2021Assignee: DASSAULT SYSTEMESInventors: Louis Dupont De Dinechin, Asma Rejeb Sfar
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Publication number: 20210049420Abstract: A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.Type: ApplicationFiled: October 30, 2020Publication date: February 18, 2021Applicant: DASSAULT SYSTEMESInventors: Louis DUPONT DE DINECHIN, Asma REJEB SFAR
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Publication number: 20200356899Abstract: A computer-implemented method of machine-learning is described that includes obtaining a test dataset of scenes. The test dataset belongs to a test domain. The method includes obtaining a domain-adaptive neural network. The domain-adaptive neural network is a machine-learned neural network taught using data obtained from a training domain. The domain-adaptive neural network is configured for inference of spatially reconfigurable objects in a scene of the test domain. The method further includes determining an intermediary domain. The intermediary domain is closer to the training domain than the test domain in terms of data distributions. The method further includes inferring, by applying the domain-adaptive neural network, a spatially reconfigurable object from a scene of the test domain transferred on the intermediary domain. Such a method constitutes an improved method of machine learning with a dataset of scenes comprising spatially reconfigurable objects.Type: ApplicationFiled: May 6, 2020Publication date: November 12, 2020Applicant: DASSAULT SYSTEMESInventors: Asma Rejeb Sfar, Malika Boulkenafed, Mariem Mezghanni
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Publication number: 20200356712Abstract: A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.Type: ApplicationFiled: May 6, 2020Publication date: November 12, 2020Applicant: Dassault SystemesInventors: Asma Rejeb Sfar, Mariem Mezghanni, Malika Boulkenafed
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Publication number: 20190243928Abstract: 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.Type: ApplicationFiled: December 28, 2018Publication date: August 8, 2019Applicant: DASSAULT SYSTEMESInventors: Asma REJEB SFAR, Louis DUPONT DE DINECHIN, Malika BOULKENAFED
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Publication number: 20190205485Abstract: The disclosure notably relates to a computer-implemented method for generating a 3D model representing a building. The method comprises providing a 2D floor plan representing a layout of the building. The method also comprises determining a semantic segmentation of the 2D floor plan. The method also comprises determining the 3D model based on the semantic segmentation. Such a method provides an improved solution for processing a 2D floor plan.Type: ApplicationFiled: December 28, 2018Publication date: July 4, 2019Applicant: DASSAULT SYSTEMESInventors: Asma REJEB SFAR, Louis Dupont De Dinechin, Malika Boulkenafed
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Patent number: 10176404Abstract: The invention notably relates to a computer-implemented method for recognizing a three-dimensional modeled object from a two-dimensional image. The method comprises providing a first set of two-dimensional images rendered from three-dimensional modeled objects, each two-dimensional image of the first set being associated to a label; providing a second set of two-dimensional images not rendered from three-dimensional objects, each two-dimensional image of the second set being associated to a label; training a model on both first and second sets; providing a similarity metric; submitting a two-dimensional image depicting at least one object; and retrieving a three-dimensional object similar to the said at least one object of the two-dimensional image submitted by using the trained model and the similarity metric.Type: GrantFiled: December 6, 2016Date of Patent: January 8, 2019Assignee: DASSAULT SYSTEMESInventors: Malika Boulkenafed, Fabrice Michel, Asma Rejeb Sfar
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Publication number: 20180322371Abstract: A computer-implemented method of signal processing comprises providing images. The method comprises for each respective one of at least a subset of the images: applying a weakly-supervised learnt function, the weakly-supervised learnt function outputting respective couples each including a respective localization and one or more respective confidence scores, each confidence score representing a probability of instantiation of a respective object category at the respective localization. The method further comprises determining, based on the output of the weakly-supervised learnt function, one or more respective annotations, each annotation including a respective localization and a respective label representing instantiation a respective object category at the respective localization. The method further comprises forming a dataset including pieces of data, each piece of data including a respective image of the subset and at least a part of the one or more annotations determined for the respective image.Type: ApplicationFiled: May 7, 2018Publication date: November 8, 2018Applicant: DASSAULT SYSTEMESInventors: Louis DUPONT DE DINECHIN, Asma REJEB SFAR
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Publication number: 20170161590Abstract: The invention notably relates to a computer-implemented method for recognizing a three-dimensional modeled object from a two-dimensional image. The method comprises providing a first set of two-dimensional images rendered from three-dimensional modeled objects, each two-dimensional image of the first set being associated to a label; providing a second set of two-dimensional images not rendered from three-dimensional objects, each two-dimensional image of the second set being associated to a label; training a model on both first and second sets; providing a similarity metric; submitting a two-dimensional image depicting at least one object; and retrieving a three-dimensional object similar to the said at least one object of the two-dimensional image submitted by using the trained model and the similarity metric.Type: ApplicationFiled: December 6, 2016Publication date: June 8, 2017Applicant: DASSAULT SYSTEMESInventors: MALIKA BOULKENAFED, FABRICE MICHEL, ASMA REJEB SFAR