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    METHOD AND SYSTEM FOR PERFORMING EYE TRACKING USING AN OFF-AXIS CAMERA
    11.
    发明申请
    METHOD AND SYSTEM FOR PERFORMING EYE TRACKING USING AN OFF-AXIS CAMERA 有权

    公开(公告)号：US20210182554A1

    公开(公告)日：2021-06-17

    申请号：US17129669

    申请日：2020-12-21

    Applicant: Magic Leap, Inc.

    Inventor： Vijay Badrinarayanan ,  Zhengyang Wu ,  Srivignesh Rajendran ,  Andrew Rabinovich

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

    Abstract: Systems and methods for estimating a gaze vector of an eye using a trained neural network. An input image of the eye may be received from a camera. The input image may be provided to the neural network. Network output data may be generated using the neural network. The network output data may include two-dimensional (2D) pupil data, eye segmentation data, and/or cornea center data. The gaze vector may be computed based on the network output data. The neural network may be previously trained by providing a training input image to the neural network, generating training network output data, receiving ground-truth (GT) data, computing error data based on a difference between the training network output data and the GT data, and modifying the neural network based on the error data.

    ROOM LAYOUT ESTIMATION METHODS AND TECHNIQUES
    12.
    发明申请
    ROOM LAYOUT ESTIMATION METHODS AND TECHNIQUES 审中-公开

    公开(公告)号：US20200234051A1

    公开(公告)日：2020-07-23

    申请号：US16844812

    申请日：2020-04-09

    Applicant: Magic Leap, Inc.

    Inventor： Chen-Yu Lee ,  Vijay Badrinarayanan ,  Tomasz Jan Malisiewicz ,  Andrew Rabinovich

    IPC: G06K9/00 ,  G06N3/04 ,  G06K9/46 ,  G06K9/34 ,  G06K9/62 ,  G06N3/08

    Abstract: Systems and methods for estimating a layout of a room are disclosed. The room layout can comprise the location of a floor, one or more walls, and a ceiling. In one aspect, a neural network can analyze an image of a portion of a room to determine the room layout. The neural network can comprise a convolutional neural network having an encoder sub-network, a decoder sub-network, and a side sub-network. The neural network can determine a three-dimensional room layout using two-dimensional ordered keypoints associated with a room type. The room layout can be used in applications such as augmented or mixed reality, robotics, autonomous indoor navigation, etc.

    Periocular test for mixed reality calibration
    13.
    发明授权
    Periocular test for mixed reality calibration 有权

    公开(公告)号：US10573042B2

    公开(公告)日：2020-02-25

    申请号：US15717747

    申请日：2017-09-27

    Applicant: Magic Leap, Inc.

    Inventor： Adrian Kaehler ,  Gary Bradski ,  Vijay Badrinarayanan

    IPC: G06T11/60 ,  G02B27/01 ,  G06K9/00 ,  G06T3/20 ,  G06K9/46

    Abstract: A wearable device can include an inward-facing imaging system configured to acquire images of a user's periocular region. The wearable device can determine a relative position between the wearable device and the user's face based on the images acquired by the inward-facing imaging system. The relative position may be used to determine whether the user is wearing the wearable device, whether the wearable device fits the user, or whether an adjustment to a rendering location of virtual object should be made to compensate for a deviation of the wearable device from its normal resting position.

    Meta-learning for multi-task learning for neural networks
    17.
    发明授权
    Meta-learning for multi-task learning for neural networks 有权

    公开(公告)号：US11853894B2

    公开(公告)日：2023-12-26

    申请号：US17344758

    申请日：2021-06-10

    Applicant: Magic Leap, Inc.

    Inventor： Andrew Rabinovich ,  Vijay Badrinarayanan ,  Srivignesh Rajendran ,  Chen-Yu Lee

    IPC: G06N3/084 ,  G06F18/21 ,  G06N3/04 ,  G06N3/044 ,  G06N3/047

    CPC classification number: G06N3/084 ,  G06F18/217 ,  G06N3/04 ,  G06N3/044 ,  G06N3/047

    Abstract: Methods and systems for meta-learning are described for automating learning of child tasks with a single neural network. The order in which tasks are learned by the neural network can affect performance of the network, and the meta-learning approach can use a task-level curriculum for multi-task training. The task-level curriculum can be learned by monitoring a trajectory of loss functions during training. The meta-learning approach can learn to adapt task loss balancing weights in the course of training to get improved performance on multiple tasks on real world datasets. Advantageously, learning to dynamically balance weights among different task losses can lead to superior performance over the use of static weights determined by expensive random searches or heuristics. Embodiments of the meta-learning approach can be used for computer vision tasks or natural language processing tasks, and the trained neural networks can be used by augmented or virtual reality devices.

    DEEP LEARNING SYSTEM FOR CUBOID DETECTION
    19.
    发明申请
    DEEP LEARNING SYSTEM FOR CUBOID DETECTION 有权

    公开(公告)号：US20220237815A1

    公开(公告)日：2022-07-28

    申请号：US17717696

    申请日：2022-04-11

    Applicant: Magic Leap, Inc.

    Inventor： Tomasz Jan Malisiewicz ,  Andrew Rabinovich ,  Vijay Badrinarayanan ,  Debidatta Dwibedi

    IPC: G06T7/70 ,  G06N3/04 ,  G06K9/62 ,  G06N3/08 ,  G06V10/44 ,  G06V20/64 ,  G06V30/194 ,  G06T7/11

    Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.

    Deep learning system for cuboid detection
    20.
    发明授权
    Deep learning system for cuboid detection 有权

    公开(公告)号：US11328443B2

    公开(公告)日：2022-05-10

    申请号：US17146799

    申请日：2021-01-12

    Applicant: Magic Leap, Inc.

    Inventor： Tomasz Jan Malisiewicz ,  Andrew Rabinovich ,  Vijay Badrinarayanan ,  Debidatta Dwibedi

    IPC: G06T7/70 ,  G06N3/04 ,  G06K9/62 ,  G06N3/08 ,  G06V10/44 ,  G06V20/64 ,  G06V30/194 ,  G06T7/11

    Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.

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