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    3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    2.
    发明申请
    3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20230136860A1

    公开(公告)日：2023-05-04

    申请号：US17452751

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: G06T17/05 ,  G06K9/62 ,  G06K9/00 ,  B60W60/00 ,  B60W40/06 ,  B60W30/14 ,  B60W30/09 ,  B60W50/06

    Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

    3D SURFACE RECONSTRUCTION WITH POINT CLOUD DENSIFICATION USING ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    3.
    发明申请
    3D SURFACE RECONSTRUCTION WITH POINT CLOUD DENSIFICATION USING ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20230136235A1

    公开(公告)日：2023-05-04

    申请号：US17452744

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: B60W60/00 ,  B60W40/06 ,  G06K9/00 ,  B60W40/105

    Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the representation may be sparse, one or more densification techniques may be applied to densify the representation of the 3D surface structure. For example, the relationship between sparse and dense projection images (e.g., 2D height maps) may be modeled with a Markov random field, and Maximum a Posterior (MAP) inference may be performed using a corresponding joint probability distribution to estimate the most likely dense values given the sparse values. The resulting dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.

    3D surface reconstruction with point cloud densification using deep neural networks for autonomous systems and applications
    4.
    发明授权
    3D surface reconstruction with point cloud densification using deep neural networks for autonomous systems and applications 有权

    公开(公告)号：US12172667B2

    公开(公告)日：2024-12-24

    申请号：US17452747

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: B60W60/00 ,  B60W40/09 ,  G06N3/02

    Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the estimated representation may be sparse, a deep neural network (DNN) may be used to predict values for a dense representation of the 3D surface structure from the sparse representation. For example, a sparse 3D point cloud may be projected to form a sparse projection image (e.g., a sparse 2D height map), which may be fed into the DNN to predict a dense projection image (e.g., a dense 2D height map). The predicted dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.

    3D SURFACE RECONSTRUCTION WITH POINT CLOUD DENSIFICATION USING DEEP NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    6.
    发明申请
    3D SURFACE RECONSTRUCTION WITH POINT CLOUD DENSIFICATION USING DEEP NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20230135088A1

    公开(公告)日：2023-05-04

    申请号：US17452747

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: B60W60/00 ,  B60W40/09 ,  G06N3/02

    Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the estimated representation may be sparse, a deep neural network (DNN) may be used to predict values for a dense representation of the 3D surface structure from the sparse representation. For example, a sparse 3D point cloud may be projected to form a sparse projection image (e.g., a sparse 2D height map), which may be fed into the DNN to predict a dense projection image (a dense 21) height map). The predicted dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.

    3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    7.
    发明申请
    3D SURFACE STRUCTURE ESTIMATION USING NEURAL NETWORKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20230139772A1

    公开(公告)日：2023-05-04

    申请号：US17452749

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: G06T17/20 ,  G06T7/11 ,  G06K9/62 ,  G06T7/40 ,  G06N3/08 ,  B60W60/00 ,  B60W50/06 ,  B60W40/06 ,  B60W30/14

    Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.

    USING NEURAL NETWORKS FOR 3D SURFACE STRUCTURE ESTIMATION BASED ON REAL-WORLD DATA FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    8.
    发明申请
    USING NEURAL NETWORKS FOR 3D SURFACE STRUCTURE ESTIMATION BASED ON REAL-WORLD DATA FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20230135234A1

    公开(公告)日：2023-05-04

    申请号：US17452752

    申请日：2021-10-28

    Applicant: NVIDIA Corporation

    Inventor： Kang Wang ,  Yue Wu ,  Minwoo Park ,  Gang Pan

    IPC: G06K9/00 ,  G06K9/62 ,  G01S17/931 ,  G01S17/89

    Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated from real-world data. For example, one or more vehicles may collect image data and LiDAR data while navigating through a real-world environment. To generate input training data, 3D surface structure estimation may be performed on captured image data to generate a sparse representation of a 3D surface structure of interest (e.g., a 3D road surface). To generate corresponding ground truth training data, captured LiDAR data may be smoothed, subject to outlier removal, subject to triangulation to filling missing values, accumulated from multiple LiDAR sensors, aligned with corresponding frames of image data, and/or annotated to identify 3D points on the 3D surface of interest, and the identified 3D points may be projected to generate a dense representation of the 3D surface structure.

    CAMERA CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    9.
    发明申请
    CAMERA CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20250022175A1

    公开(公告)日：2025-01-16

    申请号：US18349779

    申请日：2023-07-10

    Applicant: NVIDIA Corporation

    Inventor： Yue Wu ,  Cheng-Chieh Yang ,  Kang Wang ,  Ayon Sen ,  Hsin Miao

    IPC: G06T7/80 ,  G06T7/73 ,  H04N13/246

    Abstract: In various examples, sensor calibration for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that calibrate image sensors, such as cameras, using images captured by the image sensors at different time instances. For instance, a first image sensor may generate first image data representing at least two images and a second image sensor may generate second image data representing at least one image. One or more feature points may then be tracked between the images represented by the first image data and the image represented by the second image data. Additionally, the feature point(s), timestamps associated with the images, poses associated with image sensors (e.g., poses of a vehicle), and/or other information may be used to determine one or more values of one or more parameters that calibrate the first image sensor with the second image sensor.

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