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    MULTI-MODAL 3-D POSE ESTIMATION
    1.
    发明申请
    MULTI-MODAL 3-D POSE ESTIMATION 有权

    公开(公告)号：US20220156965A1

    公开(公告)日：2022-05-19

    申请号：US17505900

    申请日：2021-10-20

    Applicant: Waymo LLC

    Inventor： Jingxiao Zheng ,  Xinwei Shi ,  Alexander Gorban ,  Junhua Mao ,  Andre Liang Cornman ,  Yang Song ,  Ting Liu ,  Ruizhongtai Qi ,  Yin Zhou ,  Congcong Li ,  Dragomir Anguelov

    IPC: G06T7/73 ,  G06K9/62 ,  G06K9/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

    Neural networks for coarse- and fine-object classifications
    3.
    发明授权
    Neural networks for coarse- and fine-object classifications 有权

    公开(公告)号：US11361187B1

    公开(公告)日：2022-06-14

    申请号：US17118989

    申请日：2020-12-11

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Congcong Li ,  Yang Song

    IPC: G06K9/62 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08 ,  G06V20/56

    Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

    NEURAL NETWORKS FOR COARSE- AND FINE-OBJECT CLASSIFICATIONS
    7.
    发明申请
    NEURAL NETWORKS FOR COARSE- AND FINE-OBJECT CLASSIFICATIONS 有权

    公开(公告)号：US20220374650A1

    公开(公告)日：2022-11-24

    申请号：US17836287

    申请日：2022-06-09

    Applicant: Waymo LLC

    Inventor： Junhua Mao ,  Congcong Li ,  Yang Song

    IPC: G06K9/62 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08 ,  G06V20/56

    Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

    LEARNING POINT CLOUD AUGMENTATION POLICIES
    8.
    发明申请
    LEARNING POINT CLOUD AUGMENTATION POLICIES 有权

    公开(公告)号：US20210284184A1

    公开(公告)日：2021-09-16

    申请号：US17194072

    申请日：2021-03-05

    Applicant: Waymo LLC

    Inventor： Yang Song ,  Shuyang Cheng ,  Zijian Guo ,  Congcong Li ,  Chunyan Bai

    IPC: B60W50/06 ,  G06N3/04 ,  G06N3/08 ,  B60W60/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a point cloud augmentation policy and training a machine learning model using the point cloud augmentation policy to perform a perception task such as object detection or classification task by processing point cloud data. In general, training a machine learning model using the determined point cloud augmentation policy enables the model to more effectively perform the perception task, i.e., by generating higher quality perception outputs. When deployed within an on-board system of a vehicle, the machine learning model can further enable the on-board system to generate better-informed planning decisions which in turn result in a safer journey, even when the vehicle is navigating through unconventional environments or inclement weathers such as rain or snow.

    TRAINING DISTILLED MACHINE LEARNING MODELS USING A PRE-TRAINED FEATURE EXTRACTOR
    9.
    发明申请
    TRAINING DISTILLED MACHINE LEARNING MODELS USING A PRE-TRAINED FEATURE EXTRACTOR 有权

    公开(公告)号：US20220366263A1

    公开(公告)日：2022-11-17

    申请号：US17313655

    申请日：2021-05-06

    Applicant: Waymo LLC

    Inventor： Ming Ji ,  Edward Stephen Walker, JR. ,  Yang Song ,  Zijian Guo ,  Congcong Li

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor. In one aspect, a method includes obtaining data specifying the teacher machine learning model that is configured to perform a machine learning task; obtaining first training data; training the teacher machine learning model on the first training data to obtain a trained teacher machine learning model; generating second, automatically labeled training data by using the trained teacher machine learning model to process unlabeled training data; and training a student machine learning model to perform the machine learning task using at least the second, automatically labeled training data, wherein the student machine learning model does not include the pre-trained feature extractor and instead includes a different feature extractor having fewer parameters than the pre-trained feature extractor.

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