公开(公告)号：US20240273919A1

公开(公告)日：2024-08-15

申请号：US18647415

申请日：2024-04-26

Applicant: NVIDIA CORPORATION

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G06V20/58 ,  B60W60/00 ,  G01S17/89 ,  G01S17/931 ,  G06N3/045 ,  G06T19/00

CPC classification number: G06V20/584 ,  B60W60/0011 ,  B60W60/0016 ,  B60W60/0027 ,  G01S17/89 ,  G01S17/931 ,  G06N3/045 ,  G06T19/006 ,  G06V20/58 ,  B60W2420/403 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30261

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US11915493B2

公开(公告)日：2024-02-27

申请号：US17895940

申请日：2022-08-25

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Ryan Oldja ,  Ke Chen ,  Alexander Popov ,  Joachim Pehserl ,  Ibrahim Eden ,  Tilman Wekel ,  David Wehr ,  Ruchi Bhargava ,  David Nister

IPC: G06V20/58 ,  G01S17/931 ,  B60W60/00 ,  G01S17/89 ,  G05D1/00 ,  G06T19/00 ,  G06N3/045

CPC classification number: G06V20/584 ,  B60W60/0011 ,  B60W60/0016 ,  B60W60/0027 ,  G01S17/89 ,  G01S17/931 ,  G05D1/0088 ,  G06N3/045 ,  G06T19/006 ,  G06V20/58 ,  B60W2420/403 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30261

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

公开(公告)号：US20230357076A1

公开(公告)日：2023-11-09

申请号：US18311172

申请日：2023-05-02

Applicant: NVIDIA Corporation

Inventor： Michael Kroepfl ,  Amir Akbarzadeh ,  Ruchi Bhargava ,  Viabhav Thukral ,  Neda Cvijetic ,  Vadim Cugunovs ,  David Nister ,  Birgit Henke ,  Ibrahim Eden ,  Youding Zhu ,  Michael Grabner ,  Ivana Stojanovic ,  Yu Sheng ,  Jeffrey Liu ,  Enliang Zheng ,  Jordan Marr ,  Andrew Carley

IPC: C03C17/36

CPC classification number: C03C17/3607 ,  C03C17/3639 ,  C03C17/3644 ,  C03C17/366 ,  C03C17/3626 ,  C03C17/3668 ,  C03C17/3642 ,  C03C17/3681 ,  C03C2217/70 ,  C03C2217/216 ,  C03C2217/228 ,  C03C2217/24 ,  C03C2217/256 ,  C03C2217/281 ,  C03C2217/22 ,  C03C2217/23 ,  C03C2218/156

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

公开(公告)号：US20220237925A1

公开(公告)日：2022-07-28

申请号：US17718721

申请日：2022-04-12

Applicant: Nvidia Corporation

Inventor： Ishwar Kulkarni ,  Ibrahim Eden ,  Michael Kroepfl ,  David Nister

IPC: G06V20/58 ,  G06T15/04 ,  G01S17/89 ,  G06T11/00 ,  G06T7/521 ,  G06T7/30 ,  G06V20/56

Abstract: LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improved techniques for processing the point cloud data that has been collected. The improved techniques include mapping one or more point cloud data points into a depth map, the one or more point cloud data points being generated using one or more sensors; determining one or more mapped point cloud data points within a bounded area of the depth map, and detecting, using one or more processing units and for an environment surrounding a machine corresponding to the one or more sensors, a location of one or more entities based on the one or more mapped point cloud data points.

公开(公告)号：US20210233307A1

公开(公告)日：2021-07-29

申请号：US17228460

申请日：2021-04-12

Applicant: NVIDIA Corporation

Inventor： Philippe Bouttefroy ,  David Nister ,  Ibrahim Eden

IPC: G06T17/05 ,  G06K9/00

Abstract: In various examples, locations of directional landmarks, such as vertical landmarks, may be identified using 3D reconstruction. A set of observations of directional landmarks (e.g., images captured from a moving vehicle) may be reduced to 1D lookups by rectifying the observations to align directional landmarks along a particular direction of the observations. Object detection may be applied, and corresponding 1D lookups may be generated to represent the presence of a detected vertical landmark in an image.

公开(公告)号：US10991155B2

公开(公告)日：2021-04-27

申请号：US16385921

申请日：2019-04-16

Applicant: NVIDIA Corporation

Inventor： Philippe Bouttefroy ,  David Nister ,  Ibrahim Eden

IPC: G06T17/05 ,  G06K9/00

Abstract: In various examples, locations of directional landmarks, such as vertical landmarks, may be identified using 3D reconstruction. A set of observations of directional landmarks (e.g., images captured from a moving vehicle) may be reduced to 1D lookups by rectifying the observations to align directional landmarks along a particular direction of the observations. Object detection may be applied, and corresponding 1D lookups may be generated to represent the presence of a detected vertical landmark in an image.

公开(公告)号：US20210063200A1

公开(公告)日：2021-03-04

申请号：US17007873

申请日：2020-08-31

Applicant: NVIDIA Corporation

Inventor： Michael Kroepfl ,  Amir Akbarzadeh ,  Ruchi Bhargava ,  Vaibhav Thukral ,  Neda Cvijetic ,  Vadim Cugunovs ,  David Nister ,  Birgit Henke ,  Ibrahim Eden ,  Youding Zhu ,  Michael Grabner ,  Ivana Stojanovic ,  Yu Sheng ,  Jeffrey Liu ,  Enliang Zheng ,  Jordan Marr ,  Andrew Carley

IPC: G01C21/00 ,  G06N3/02

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

公开(公告)号：US20210026355A1

公开(公告)日：2021-01-28

申请号：US16938706

申请日：2020-07-24

Applicant: NVIDIA Corporation

Inventor： Ke Chen ,  Nikolai Smolyanskiy ,  Alexey Kamenev ,  Ryan Oldja ,  Tilman Wekel ,  David Nister ,  Joachim Pehserl ,  Ibrahim Eden ,  Sangmin Oh ,  Ruchi Bhargava

IPC: G05D1/00 ,  G06T7/10 ,  G06T5/50 ,  G06K9/00 ,  G06K9/62

Abstract: A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.