Patents by Inventor Arumugam Kalai Kannan

Arumugam Kalai Kannan 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: 20240127538
    Abstract: This document describes scene understanding for cross reality systems using occupancy grids. In one aspect, a method includes recognizing one or more objects in a model of a physical environment generated using images of the physical environment. For each object, a bounding box is fit around the object. An occupancy grid that includes a multiple cells is generated within the bounding box around the object. A value is assigned to each cell of the occupancy grid based on whether the cell includes a portion of the object. An object representation that includes information describing the occupancy grid for the object is generated. The object representations are sent to one or more devices.
    Type: Application
    Filed: February 3, 2022
    Publication date: April 18, 2024
    Inventors: Divya Ramnath, Shiyu Dong, Siddharth Choudhary, Siddharth Mahendran, Arumugam Kalai Kannan, Prateek Singhal, Khushi Gupta
  • Patent number: 11704806
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.
    Type: Grant
    Filed: January 12, 2022
    Date of Patent: July 18, 2023
    Assignee: Magic Leap, Inc.
    Inventors: Siddharth Choudhary, Divya Ramnath, Shiyu Dong, Siddharth Mahendran, Arumugam Kalai Kannan, Prateek Singhal, Khushi Gupta, Nitesh Sekhar, Manushree Gangwar
  • Publication number: 20220139057
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.
    Type: Application
    Filed: January 12, 2022
    Publication date: May 5, 2022
    Inventors: Siddharth Choudhary, Divya Ramnath, Shiyu Dong, Siddharth Mahendran, Arumugam Kalai Kannan, Prateek Singhal, Khushi Gupta, Nitesh Sekhar, Manushree Gangwar
  • Patent number: 11257300
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.
    Type: Grant
    Filed: June 12, 2020
    Date of Patent: February 22, 2022
    Assignee: Magic Leap, Inc.
    Inventors: Siddharth Choudhary, Divya Ramnath, Shiyu Dong, Siddharth Mahendran, Arumugam Kalai Kannan, Prateek Singhal, Khushi Gupta, Nitesh Sekhar, Manushree Gangwar
  • Publication number: 20200394848
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.
    Type: Application
    Filed: June 12, 2020
    Publication date: December 17, 2020
    Inventors: Siddharth Choudhary, Divya Ramnath, Shiyu Dong, Siddarth Mahendran, Arumugam Kalai Kannan, Prateek Singhal, Khushi Gupta, Nitesh Sekhar, Manushree Gangwar