公开(公告)号：US20220012466A1

公开(公告)日：2022-01-13

申请号：US16926096

申请日：2020-07-10

Applicant: Ehsan TAGHAVI ,  Amirhosein NABATCHIAN ,  Bingbing LIU

Inventor： Ehsan TAGHAVI ,  Amirhosein NABATCHIAN ,  Bingbing LIU

IPC: G06K9/00 ,  G06T3/60 ,  G06T7/20 ,  G06K9/62

Abstract: A system and method for generating a bounding box for an object in proximity to a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud representative of an environment; receiving a two-dimensional (2D) image of the environment; processing the 3D point cloud to identify an object cluster of 3D data points for a 3D object in the 3D point cloud; processing the 2D image to detect a 2D object in the 2D image and generate information regarding the 2D object from the 2D image; and when the 3D object and the 2D object correspond to the same object in the environment: generating a bird's eye view (BEV) bounding box for the object based on the object cluster of 3D data points and the information from the 2D image.

公开(公告)号：US20210347378A1

公开(公告)日：2021-11-11

申请号：US16871711

申请日：2020-05-11

Applicant: Amirhosein NABATCHIAN ,  Ehsan TAGHAVI

Inventor： Amirhosein NABATCHIAN ,  Ehsan TAGHAVI

IPC: B60W60/00 ,  G06K9/62 ,  G06K9/00 ,  G06K9/03 ,  G06K9/46 ,  G06T7/20 ,  G05D1/02

Abstract: A system and method for generating an importance occupancy grid map (OGM) for a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud; receiving a binary map, the binary map associated with a set of GPS coordinates of the vehicle; receiving information representative of a planned path for the vehicle; and generating an importance OGM based on the 3D point cloud, the binary map, and the planned path for the vehicle using a map generation module.

公开(公告)号：US20240078787A1

公开(公告)日：2024-03-07

申请号：US17902728

申请日：2022-09-02

Applicant: Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

Inventor： Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

IPC: G06V10/764 ,  G06T7/73 ,  G06T15/20 ,  G06V10/25 ,  G06V10/26 ,  G06V10/74 ,  G06V10/762 ,  G06V20/58

CPC classification number: G06V10/764 ,  G06T7/73 ,  G06T15/205 ,  G06V10/25 ,  G06V10/26 ,  G06V10/74 ,  G06V10/762 ,  G06V20/58

Abstract: Method and system for processing a point cloud frame representing a real-world scene that includes one or more objects, including assigning data-element-level classification labels to data elements that each respectively represent one or more points included in the point cloud frame, estimating an approximate position of a first object instance represented in the point cloud frame, assigning an object-instance-level classification label to the first object instance, selecting, for the first object instance, a subgroup of the data elements based on the approximate position, selecting from the subgroup a first cluster of data elements that have assigned data-element-level classification labels that match the object-instance-level classification label assigned to the first object instance, and outputting an object instance list that indicates, for the first object instance, the first cluster of data elements, and the object-instance-level classification label assigned to the first object instance.

公开(公告)号：US20230169348A1

公开(公告)日：2023-06-01

申请号：US18160662

申请日：2023-01-27

Applicant: Martin Ivanov GERDZHEV ,  Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

Inventor： Martin Ivanov GERDZHEV ,  Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

IPC: G06N3/084

CPC classification number: G06N3/084

Abstract: Method and system for computing a total variation loss for use in backpropagation during training a neural network which individually classifies data points, comprising: predicting, using a neural network, a respective label for each data point in a set of input data points; determining a variation indicator that indicates a variance between: (i) smoothness of the predicted labels among neighboring data points and (ii) smoothness of the ground truth labels among the same neighboring data points; and computing the total variation loss based on the variation indicator.

公开(公告)号：US20220300681A1

公开(公告)日：2022-09-22

申请号：US17203718

申请日：2021-03-16

Applicant: Yuan REN ,  Ehsan TAGHAVI ,  Bingbing LIU

Inventor： Yuan REN ,  Ehsan TAGHAVI ,  Bingbing LIU

IPC: G06F30/27 ,  G06T7/50

Abstract: Devices, systems, methods, and media are described for point cloud data augmentation using model injection, for the purpose of training machine learning models to perform point cloud segmentation and object detection. A library of surface models is generated from point cloud object instances in LIDAR-generated point cloud frames. The surface models can be used to inject new object instances into target point cloud frames at an arbitrary location within the target frame to generate new, augmented point cloud data. The augmented point cloud data may then be used as training data to improve the accuracy of a machine learned model trained using a machine learning algorithm to perform a segmentation and/or object detection task.

公开(公告)号：US20230410530A1

公开(公告)日：2023-12-21

申请号：US17827963

申请日：2022-05-30

Applicant: Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

Inventor： Ehsan TAGHAVI ,  Ryan RAZANI ,  Bingbing LIU

IPC: G06V20/58 ,  G06V10/26 ,  G06V10/762 ,  G06V10/764 ,  G01S17/89 ,  B60W30/00

CPC classification number: G06V20/58 ,  G06V10/26 ,  G06V10/762 ,  B60W2420/52 ,  G01S17/89 ,  B60W30/00 ,  G06V10/764

Abstract: Devices, systems, methods, and media are disclosed for performing an object detection task comprising: obtaining a semantic segmentation map representing a real-world space, the semantic segmentation map including an array of elements that each represent a respective location in the real-world space and are assigned a respective element classification label; clustering groups of the elements based on the assigned respective element classification labels to identify at least a first cluster of elements that have each been assigned the same respective element classification label; generating, based on a location of the first cluster within the semantic segmentation map, at least one anchor that defines a respective probable object location of a first dynamic object; and generating, based on the semantic segmentation map and the at least one anchor, a respective bounding box and object instance classification label for the first dynamic object.