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    Weakly supervised learning of 3D human poses from 2D poses
    13.
    发明授权
    Weakly supervised learning of 3D human poses from 2D poses 有权

    公开(公告)号：US11256962B2

    公开(公告)日：2022-02-22

    申请号：US16815206

    申请日：2020-03-11

    Applicant: Tata Consultancy Services Limited

    Inventor： Sandika Biswas ,  Sanjana Sinha ,  Kavya Gupta ,  Brojeshwar Bhowmick

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08

    Abstract: Estimating 3D human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from single view. Recent deep learning based methods show promising results by using supervised learning on 3D pose annotated datasets. However, the lack of large-scale 3D annotated training data makes the 3D pose estimation difficult in-the-wild. Embodiments of the present disclosure provide a method which can effectively predict 3D human poses from only 2D pose in a weakly-supervised manner by using both ground-truth 3D pose and ground-truth 2D pose based on re-projection error minimization as a constraint to predict the 3D joint locations. The method may further utilize additional geometric constraints on reconstructed body parts to regularize the pose in 3D along with minimizing re-projection error to improvise on estimating an accurate 3D pose.

    Systems and methods for coupled representation using transform learning for solving inverse problems
    14.
    发明授权
    Systems and methods for coupled representation using transform learning for solving inverse problems 有权

    公开(公告)号：US11216692B2

    公开(公告)日：2022-01-04

    申请号：US16502760

    申请日：2019-07-03

    Applicant: Tata Consultancy Services Limited

    Inventor： Kavya Gupta ,  Brojeshwar Bhowmick ,  Angshul Majumdar

    IPC: G06K9/62

    Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

    SYSTEM AND METHOD FOR TRACKING BODY JOINTS
    16.
    发明申请
    SYSTEM AND METHOD FOR TRACKING BODY JOINTS 审中-公开

    公开(公告)号：US20190008421A1

    公开(公告)日：2019-01-10

    申请号：US15908731

    申请日：2018-02-28

    Applicant: Tata Consultancy Services Limited

    Inventor： Sanjana Sinha ,  Brojeshwar Bhowmick ,  Aniruddha Sinha ,  Abhijit Das

    IPC: A61B5/11 ,  G06T7/246 ,  G06K9/00

    Abstract: Body joint tracking is applied in various industries and medical field. In body joint tracking, marker less devices plays an important role. However, the marker less devices are facing some challenges in providing optimal tracking due to occlusion, ambiguity, lighting conditions, dynamic objects etc. System and method of the present disclosure provides an optimized body joint tracking. Here, motion data pertaining to a first set of motion frames from a motion sensor are received. Further, the motion data are processed to obtain a plurality of 3 dimensional cylindrical models. Here, every cylindrical model among the plurality of 3 dimensional cylindrical model represents a body segment. The coefficients associated with the plurality of 3 dimensional cylindrical models are initialized to obtain a set of initialized cylindrical models. A set of dynamic coefficients associated with the initialized cylindrical models are utilized to track joint motion trajectories of a set of subsequent frames.

    Constructing a 3D structure
    17.
    发明授权
    Constructing a 3D structure 有权

    公开(公告)号：US09865061B2

    公开(公告)日：2018-01-09

    申请号：US14493959

    申请日：2014-09-23

    Applicant: Tata Consultancy Services Limited

    Inventor： Brojeshwar Bhowmick ,  Apurbaa Mallik ,  Arindam Saha

    IPC: G06T7/80 ,  G06T7/593 ,  G06K9/20 ,  G06T7/00 ,  G06T7/579

    CPC classification number: G06T7/593 ,  G06K9/209 ,  G06K2209/40 ,  G06T7/579 ,  G06T7/80 ,  G06T7/97 ,  G06T2200/08 ,  G06T2207/10021

    Abstract: Disclosed is a method and system for constructing a 3D structure. The system of the present disclosure comprises an image capturing unit for capturing images of an object. The system comprises of a gyroscope, a magnetometer, and an accelerometer for determining extrinsic camera parameters, wherein the extrinsic camera parameters comprise a rotation and a translation of the images. Further the system determines an internal calibration matrix once. The system uses the extrinsic camera parameters and the internal calibration matrix for determining a fundamental matrix. The system extracts features of the images for establishing point correspondences between the images. Further, the point correspondences are filtered using the fundamental matrix for generating filtered point correspondences. The filtered point correspondences are triangulated for determining 3D points representing the 3D structure. Further, the 3D structure may be optimized for eliminating reprojection errors associated with the 3D structure.

    Method and system for generating 3D mesh of a scene using RGBD image sequence
    18.
    发明授权
    Method and system for generating 3D mesh of a scene using RGBD image sequence 有权

    公开(公告)号：US11941760B2

    公开(公告)日：2024-03-26

    申请号：US17807339

    申请日：2022-06-16

    Applicant: Tata Consultancy Services Limited

    Inventor： Swapna Agarwal ,  Soumyadip Maity ,  Hrishav Bakul Barua ,  Brojeshwar Bhowmick

    IPC: G06T17/20 ,  G06T7/70

    CPC classification number: G06T17/20 ,  G06T7/70 ,  G06T2207/10024

    Abstract: Traditional machine learning (ML) based systems used for scene recognition and object recognition have the disadvantage that they require huge quantity of labeled data to generate data models for the purpose of aiding the scene and object recognition. The disclosure herein generally relates to image processing, and, more particularly, to method and system for generating 3D mesh generation using planar and non-planar data. The system extracts planar point cloud and non-planar point cloud from each RGBD image in a sequence of RGBD images fetched as input, and then generates a planar mesh and a non-planar mesh for planar and non-planar objects in the image. A mesh representation is generated by merging the planar mesh and the non-planar mesh. Further, an incremental merging of the mesh representation is performed on the sequence of RGBD images, based on an estimated camera pose information, to generate representation of the scene.

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