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公开(公告)号：US10983217B2
公开(公告)日：2021-04-20
申请号：US16206465
申请日：2018-11-30
Applicant: Ehsan Nezhadarya , Amirhosein Nabatchian , Bingbing Liu
Inventor： Ehsan Nezhadarya , Amirhosein Nabatchian , Bingbing Liu
Abstract: Methods and apparatuses for generating a frame of semantically labeled 2D data are described. A frame of sparse 3D data is generated from a frame of sparse 3D data. Semantic labels are assigned to the frame of dense 3D data, based on a set of 3D bounding boxes determined for the frame of sparse 3D data. Semantic labels are assigned to a corresponding frame of 2D data based on a mapping between the frame of sparse 3D data and the frame of 2D data. The mapping is used to map a 3D data point in the frame of dense 3D data to a mapped 2D data point in the frame of 2D data. The semantic label assigned to the 3D data point is assigned to the mapped 2D data point. The frame of semantically labeled 2D data, including the assigned semantic labels, is outputted.
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2.发明授权公开(公告)号：US10970871B2
公开(公告)日：2021-04-06
申请号：US16380273
申请日：2019-04-10
Applicant: Ehsan Nezhadarya , Yang Liu , Bingbing Liu
Inventor： Ehsan Nezhadarya , Yang Liu , Bingbing Liu
Abstract: Upon receiving a set of two-dimensional data points representing an object in an environment, a bounding box estimator estimates a bounding box vector representative of a two-dimensional version of the object that is represented by the two-dimensional data points.
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3.发明申请公开(公告)号：US20200151512A1
公开(公告)日：2020-05-14
申请号：US16184570
申请日：2018-11-08
Applicant: Eduardo R. Corral-Soto , Ehsan Nezhadarya , Bingbing Liu
Inventor： Eduardo R. Corral-Soto , Ehsan Nezhadarya , Bingbing Liu
IPC: G06K9/62
Abstract: Methods and systems for encoding 3D data for use with 2D convolutional neural networks (CNNs) are described. A set of 3D data is encoded into a set of one or more arrays. A 2D index of the arrays is calculated by projecting 3D coordinates of the 3D point onto a 2D image plane that is defined by a set of defined virtual camera parameters. The virtual camera parameters include a camera projection matrix defining the 2D image plane. Each 3D coordinate of the point is stored in the arrays at the calculated 2D index. The set of encoded arrays is provided for input to a 2D CNN, for training or inference.
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Patent Agency Ranking
公开(公告)号：US11676005B2
公开(公告)日：2023-06-13
申请号：US16191011
申请日：2018-11-14
Applicant: Ehsan Nezhadarya , Ehsan Taghavi , Bingbing Liu
Inventor： Ehsan Nezhadarya , Ehsan Taghavi , Bingbing Liu
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Methods and systems for deep neural networks using dynamically selected feature-relevant points from a point cloud are described. A plurality of multidimensional feature vectors arranged in a point-feature matrix are received. Each row of the point-feature matrix corresponds to a respective one of the multidimensional feature vectors, and each column of the point-feature matrix corresponds to a respective feature. Each multidimensional feature vector represents a respective unordered point from a point cloud and each multidimensional feature vector includes a respective plurality of feature-correlated values, each feature-correlated value represents a correlation extent of the respective feature. A reduced-max matrix having a selected plurality of feature-relevant vectors is generated. The feature-relevant vectors are selected by, for each respective feature, identifying a respective multidimensional feature vector in the point-feature matrix having a maximum feature-correlated value associated with the respective feature. The reduced-max matrix is output to at least one neural network layer.
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5.发明申请公开(公告)号：US20200151557A1
公开(公告)日：2020-05-14
申请号：US16191011
申请日：2018-11-14
Applicant: Ehsan Nezhadarya , Ehsan Taghavi , Bingbing Liu
Inventor： Ehsan Nezhadarya , Ehsan Taghavi , Bingbing Liu
IPC: G06N3/08
Abstract: Methods and systems for deep neural networks using dynamically selected feature-relevant points from a point cloud are described. A plurality of multidimensional feature vectors arranged in a point-feature matrix are received. Each row of the point-feature matrix corresponds to a respective one of the multidimensional feature vectors, and each column of the point-feature matrix corresponds to a respective feature. Each multidimensional feature vector represents a respective unordered point from a point cloud and each multidimensional feature vector includes a respective plurality of feature-correlated values, each feature-correlated value represents a correlation extent of the respective feature. A reduced-max matrix having a selected plurality of feature-relevant vectors is generated. The feature-relevant vectors are selected by, for each respective feature, identifying a respective multidimensional feature vector in the point-feature matrix having a maximum feature-correlated value associated with the respective feature. The reduced-max matrix is output to at least one neural network layer.
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6.发明授权公开(公告)号：US10915793B2
公开(公告)日：2021-02-09
申请号：US16184570
申请日：2018-11-08
Applicant: Eduardo R. Corral-Soto , Ehsan Nezhadarya , Bingbing Liu
Inventor： Eduardo R. Corral-Soto , Ehsan Nezhadarya , Bingbing Liu
IPC: G06K9/62
Abstract: Methods and systems for encoding 3D data for use with 2D convolutional neural networks (CNNs) are described. A set of 3D data is encoded into a set of one or more arrays. A 2D index of the arrays is calculated by projecting 3D coordinates of the 3D point onto a 2D image plane that is defined by a set of defined virtual camera parameters. The virtual camera parameters include a camera projection matrix defining the 2D image plane. Each 3D coordinate of the point is stored in the arrays at the calculated 2D index. The set of encoded arrays is provided for input to a 2D CNN, for training or inference.
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公开(公告)号：US20200174132A1
公开(公告)日：2020-06-04
申请号：US16206465
申请日：2018-11-30
Applicant: Ehsan Nezhadarya , Amirhosein Nabatchian , Bingbing Liu
Inventor： Ehsan Nezhadarya , Amirhosein Nabatchian , Bingbing Liu
Abstract: Methods and apparatuses for generating a frame of semantically labeled 2D data are described. A frame of sparse 3D data is generated from a frame of sparse 3D data. Semantic labels are assigned to the frame of dense 3D data, based on a set of 3D bounding boxes determined for the frame of sparse 3D data. Semantic labels are assigned to a corresponding frame of 2D data based on a mapping between the frame of sparse 3D data and the frame of 2D data. The mapping is used to map a 3D data point in the frame of dense 3D data to a mapped 2D data point in the frame of 2D data. The semantic label assigned to the 3D data point is assigned to the mapped 2D data point. The frame of semantically labeled 2D data, including the assigned semantic labels, is outputted.
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8.发明申请公开(公告)号：US20200082560A1
公开(公告)日：2020-03-12
申请号：US16380273
申请日：2019-04-10
Applicant: Ehsan Nezhadarya , Yang Liu , Bingbing Liu
Inventor： Ehsan Nezhadarya , Yang Liu , Bingbing Liu
Abstract: Upon receiving a set of two-dimensional data points representing an object in an environment, a bounding box estimator estimates a bounding box vector representative of a two-dimensional version of the object that is represented by the two-dimensional data points.
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