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    UNSUPERVISED DEPTH PREDICTION NEURAL NETWORKS
    2.
    发明申请
    UNSUPERVISED DEPTH PREDICTION NEURAL NETWORKS 有权

    公开(公告)号：US20210319578A1

    公开(公告)日：2021-10-14

    申请号：US17272419

    申请日：2019-09-05

    Applicant: Google LLC

    Inventor： Vincent Michael Casser ,  Soeren Pirk ,  Reza Mahjourian ,  Anelia Angelova

    IPC: G06T7/55 ,  G06T7/246 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04

    Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.

    Training neural networks using consistency measures
    3.
    发明授权
    Training neural networks using consistency measures 有权

    公开(公告)号：US11544498B2

    公开(公告)日：2023-01-03

    申请号：US17194090

    申请日：2021-03-05

    Applicant: Google LLC

    Inventor： Ariel Gordon ,  Soeren Pirk ,  Anelia Angelova ,  Vincent Michael Casser ,  Yao Lu ,  Anthony Brohan ,  Zhao Chen ,  Jan Dlabal

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.

    Unsupervised depth prediction neural networks
    5.
    发明授权
    Unsupervised depth prediction neural networks 有权

    公开(公告)号：US12260576B2

    公开(公告)日：2025-03-25

    申请号：US18367888

    申请日：2023-09-13

    Applicant: Google LLC

    Inventor： Vincent Michael Casser ,  Soeren Pirk ,  Reza Mahjourian ,  Anelia Angelova

    IPC: G06T7/00 ,  G06N3/045 ,  G06N3/088 ,  G06T3/18 ,  G06T7/246 ,  G06T7/55

    Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.

    ROBOT NAVIGATION IN DEPENDENCE ON GESTURE(S) OF HUMAN(S) IN ENVIRONMENT WITH ROBOT
    7.
    发明公开
    ROBOT NAVIGATION IN DEPENDENCE ON GESTURE(S) OF HUMAN(S) IN ENVIRONMENT WITH ROBOT 审中-公开

    公开(公告)号：US20240094736A1

    公开(公告)日：2024-03-21

    申请号：US18240124

    申请日：2023-08-30

    Applicant: GOOGLE LLC

    Inventor： Catie Cuan ,  Tsang-Wei Lee ,  Anthony G. Francis, JR. ,  Alexander Toshev ,  Soeren Pirk

    IPC: G05D1/02 ,  G06V10/80 ,  G06V10/82 ,  G06V40/20

    CPC classification number: G05D1/0221 ,  G05D1/0248 ,  G05D1/0274 ,  G06V10/80 ,  G06V10/82 ,  G06V40/28

    Abstract: Training and/or utilizing a high-level neural network (NN) model, such as a sequential NN model. The high-level NN model, when trained, can be used to process a sequence of consecutive state data instances (e.g., N most recent, including a current state date instance) to generate a sequence of outputs that indicate a sequence of position deltas. The sequence of position deltas can be used to generate an intermediate target position for navigation and, optionally, an intermediate target orientation that corresponds to the intermediate target position. The intermediate target position and, optionally, the intermediate target orientation, can be provided to a low-level navigation policy, such as an MPC policy, and used by the low-level navigation policy as its goal position (and optionally goal orientation) for a plurality of iterations (e.g., until a new intermediate target position (and optionally new target orientation) is generated using the high-level NN model.

    Unsupervised depth prediction neural networks
    8.
    发明授权
    Unsupervised depth prediction neural networks 有权

    公开(公告)号：US11783500B2

    公开(公告)日：2023-10-10

    申请号：US17272419

    申请日：2019-09-05

    Applicant: Google LLC

    Inventor： Vincent Michael Casser ,  Soeren Pirk ,  Reza Mahjourian ,  Anelia Angelova

    IPC: G06T7/00 ,  G06T7/55 ,  G06T7/246 ,  G06N3/088 ,  G06T3/00 ,  G06N3/045

    CPC classification number: G06T7/55 ,  G06N3/045 ,  G06N3/088 ,  G06T3/0093 ,  G06T7/248 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A system for generating a depth output for an image is described. The system receives input images that depict the same scene, each input image including one or more potential objects. The system generates, for each input image, a respective background image and processes the background images to generate a camera motion output that characterizes the motion of the camera between the input images. For each potential object, the system generates a respective object motion output for the potential object based on the input images and the camera motion output. The system processes a particular input image of the input images using a depth prediction neural network (NN) to generate a depth output for the particular input image, and updates the current values of parameters of the depth prediction NN based on the particular depth output, the camera motion output, and the object motion outputs for the potential objects.

    Future semantic segmentation prediction using 3D structure
    9.
    发明授权
    Future semantic segmentation prediction using 3D structure 有权

    公开(公告)号：US11100646B2

    公开(公告)日：2021-08-24

    申请号：US16562819

    申请日：2019-09-06

    Applicant: Google LLC

    Inventor： Suhani Vora ,  Reza Mahjourian ,  Soeren Pirk ,  Anelia Angelova

    IPC: G06T7/00 ,  G06T7/11 ,  G06T7/246 ,  G06T7/55 ,  G06K9/00 ,  G06N3/08

    Abstract: A method for generating a predicted segmentation map for potential objects in a future scene depicted in a future image is described. The method includes receiving input images that depict a same scene; processing a current input image to generate a segmentation map for potential objects in the current input image and a respective depth map; generating a point cloud for the current input image; processing the input images to generate, for each pair of two input images in the sequence, a respective ego-motion output that characterizes motion of the camera between the two input images; processing the ego-motion outputs to generate a future ego-motion output; processing the point cloud of the current input image and the future ego-motion output to generate a future point cloud; and processing the future point cloud to generate the predicted segmentation map for potential objects in the future scene depicted in the future image.

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