Patents by Inventor Daniel DeTone

Daniel DeTone has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20190147341
    Abstract: Systems, devices, and methods for training a neural network and performing image interest point detection and description using the neural network. The neural network may include an interest point detector subnetwork and a descriptor subnetwork. An optical device may include at least one camera for capturing a first image and a second image. A first set of interest points and a first descriptor may be calculated using the neural network based on the first image, and a second set of interest points and a second descriptor may be calculated using the neural network based on the second image. A homography between the first image and the second image may be determined based on the first and second sets of interest points and the first and second descriptors. The optical device may adjust virtual image light being projected onto an eyepiece based on the homography.
    Type: Application
    Filed: November 14, 2018
    Publication date: May 16, 2019
    Applicant: Magic Leap, Inc.
    Inventors: Andrew Rabinovich, Daniel DeTone, Tomasz Jan Malisiewicz
  • Patent number: 10255529
    Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
    Type: Grant
    Filed: March 13, 2017
    Date of Patent: April 9, 2019
    Assignee: Magic Leap, Inc.
    Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz
  • Publication number: 20190005670
    Abstract: Augmented reality devices and methods for computing a homography based on two images. One method may include receiving a first image based on a first camera pose and a second image based on a second camera pose, generating a first point cloud based on the first image and a second point cloud based on the second image, providing the first point cloud and the second point cloud to a neural network, and generating, by the neural network, the homography based on the first point cloud and the second point cloud. The neural network may be trained by generating a plurality of points, determining a 3D trajectory, sampling the 3D trajectory to obtain camera poses viewing the points, projecting the points onto 2D planes, comparing a generated homography using the projected points to the ground-truth homography and modifying the neural network based on the comparison.
    Type: Application
    Filed: June 27, 2018
    Publication date: January 3, 2019
    Applicant: Magic Leap, Inc.
    Inventors: Daniel DeTone, Tomasz Jan Malisiewicz, Andrew Rabinovich
  • Publication number: 20180053056
    Abstract: A head-mounted augmented reality (AR) device can include a hardware processor programmed to receive different types of sensor data from a plurality of sensors (e.g., an inertial measurement unit, an outward-facing camera, a depth sensing camera, an eye imaging camera, or a microphone); and determining an event of a plurality of events using the different types of sensor data and a hydra neural network (e.g., face recognition, visual search, gesture identification, semantic segmentation, object detection, lighting detection, simultaneous localization and mapping, relocalization).
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: Andrew Rabinovich, Tomasz Jan Malisiewicz, Daniel DeTone
  • Publication number: 20170337470
    Abstract: A method for generating inputs for a neural network based on an image includes receiving the image, identifying a position within the image, and identifying a subset of the image at the position. The subset of the image is defined by a first set of corners. The method also includes perturbing at least one of the first set of corners to form a second set of corners. The second set of corners defines a modified subset of the image. The method further includes determining a homography based on a comparison between the subset of the image and the modified subset of the image, generating a transformed image by applying the homography to the image, and identifying a subset of the transformed image at the position.
    Type: Application
    Filed: May 19, 2017
    Publication date: November 23, 2017
    Applicant: Magic Leap, Inc.
    Inventors: Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
  • Publication number: 20170262737
    Abstract: The present disclosure provides an improved approach to implement structure learning of neural networks by exploiting correlations in the data/problem the networks aim to solve. A greedy approach is described that finds bottlenecks of information gain from the bottom convolutional layers all the way to the fully connected layers. Rather than simply making the architecture deeper, additional computation and capacitance is only added where it is required.
    Type: Application
    Filed: March 13, 2017
    Publication date: September 14, 2017
    Applicant: Magic Leap, Inc.
    Inventors: Andrew Rabinovich, Vijay Badrinarayanan, Daniel DeTone, Srivignesh Rajendran, Douglas Bertram Lee, Tomasz Malisiewicz