Patents by Inventor Aaditya Popli

Aaditya Popli 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).

  • Methods and systems for generating end-to-end model to estimate 3-dimensional(3-D) pose of object
    Patent number: 12033352
    Abstract: The present disclosure herein provides methods and systems that solves the technical problems of generating an efficient, accurate and light-weight 3-Dimensional (3-D) pose estimation framework for estimating the 3-D pose of an object present in an image used for the 3-dimensional (3D) model registration using deep learning, by training a composite network model with both shape features and image features of the object. The composite network model includes a graph neural network (GNN) for capturing the shape features of the object and a convolution neural network (CNN) for capturing the image features of the object. The graph neural network (GNN) utilizes the local neighbourhood information through the image features of the object and at the same time maintaining global shape property through the shape features of the object, to estimate the 3-D pose of the object.
    Type: Grant
    Filed: November 30, 2021
    Date of Patent: July 9, 2024
    Assignee: Tata Consultancy Limited Services
    Inventors: Ramya Sugnana Murthy Hebbalaguppe, Meghal Dani, Aaditya Popli
  • METHODS AND SYSTEMS FOR GENERATING END-TO-END MODEL TO ESTIMATE 3-DIMENSIONAL(3-D) POSE OF OBJECT
    Publication number: 20220222852
    Abstract: The present disclosure herein provides methods and systems that solves the technical problems of generating an efficient, accurate and light-weight 3-Dimensional (3-D) pose estimation framework for estimating the 3-D pose of an object present in an image used for the 3-dimensional (3D) model registration using deep learning, by training a composite network model with both shape features and image features of the object. The composite network model includes a graph neural network (GNN) for capturing the shape features of the object and a convolution neural network (CNN) for capturing the image features of the object. The graph neural network (GNN) utilizes the local neighbourhood information through the image features of the object and at the same time maintaining global shape property through the shape features of the object, to estimate the 3-D pose of the object.
    Type: Application
    Filed: November 30, 2021
    Publication date: July 14, 2022
    Applicant: Tata Consultancy Services Limited
    Inventors: Ramya Sugnana Murthy HEBBALAGUPPE, Meghal Dani, Aaditya Popli