公开(公告)号：US20250022217A1

公开(公告)日：2025-01-16

申请号：US18351917

申请日：2023-07-13

Applicant: NVIDIA Corporation

Inventor： Abhishek Bajpayee ,  Sai Krishnan Chandrasekar ,  Xudong Chen ,  Hae Jong Seo ,  Siddharth Kothiyal

IPC: G06T17/00

Abstract: Systems and methods are disclosed that relate to object detection and to generating detected object representations. Sensor data corresponding to a scene may be obtained that may represent one or more objects. A tensor may be generated based at least on the sensor data, where the tensor may represent the one or more objects and may include respective predicted 3D characteristics of the one or more objects. The tensor may be represented in 2D space and may be decoded to generate 3D representations of objects using, for example, one or more curve fitting algorithms.

公开(公告)号：US11508049B2

公开(公告)日：2022-11-22

申请号：US16570187

申请日：2019-09-13

Applicant: NVIDIA Corporation

Inventor： Hae-Jong Seo ,  Abhishek Bajpayee ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/98

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

公开(公告)号：US20200090322A1

公开(公告)日：2020-03-19

申请号：US16570187

申请日：2019-09-13

Applicant: NVIDIA Corporation

Inventor： Hae-Jong Seo ,  Abhishek Bajpayee ,  David Nister ,  Minwoo Park ,  Neda Cvijetic

IPC: G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06K9/03

Abstract: In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.