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    METHODS AND SYSTEMS FOR OBTAINING A SCALE REFERENCE AND MEASUREMENTS OF 3D OBJECTS FROM 2D PHOTOS
    1.
    发明申请
    METHODS AND SYSTEMS FOR OBTAINING A SCALE REFERENCE AND MEASUREMENTS OF 3D OBJECTS FROM 2D PHOTOS 有权

    公开(公告)号：US20230052613A1

    公开(公告)日：2023-02-16

    申请号：US17794255

    申请日：2021-01-22

    Applicant: Jin Chong KOH ,  Visualize K.K.

    Inventor： Kyohei Kamiyama ,  Chong Jin Koh

    IPC: G06T19/20 ,  H04N5/232 ,  G06T7/60 ,  G06T17/20

    Abstract: Disclosed are systems and methods for obtaining a scale factor and 3D measurements of objects from a series of 2D images. An object to be measured is selected from a menu of an Augmented Reality (AR) based measurement application being executed by a mobile computing device. Measurement instructions corresponding to the selected object are retrieved and used to generate a series of image capture screens. A series of image capture screens assist the user in positioning the device relative to the object in a plurality of imaging positions to capture the series of 2D images. The images are used to determine one or more scale factors and to build a complete scaled 3D model of the object in virtual 3D space. The 3D model is used to generate one or more measurements of the object.

    Methods and systems for predicting pressure maps of 3D objects from 2D photos using deep learning
    2.
    发明授权
    Methods and systems for predicting pressure maps of 3D objects from 2D photos using deep learning 有权

    公开(公告)号：US11574421B2

    公开(公告)日：2023-02-07

    申请号：US17637245

    申请日：2020-08-27

    Applicant: Visualize K.K.

    Inventor： Chong Jin Koh ,  Kyohei Kamiyama

    IPC: G06V10/00 ,  G06T7/00 ,  A61B5/103 ,  G06N3/08 ,  G06T17/20

    Abstract: A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation DLN based on the structured 3D model and the object parameters. The pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.

    METHODS AND SYSTEMS FOR PREDICTING PRESSURE MAPS OF 3D OBJECTS FROM 2D PHOTOS USING DEEP LEARNING
    4.
    发明申请
    METHODS AND SYSTEMS FOR PREDICTING PRESSURE MAPS OF 3D OBJECTS FROM 2D PHOTOS USING DEEP LEARNING 有权

    公开(公告)号：US20220270297A1

    公开(公告)日：2022-08-25

    申请号：US17637245

    申请日：2020-08-27

    Applicant: Visualize K.K.

    Inventor： Chong Jin Koh ,  Kyohei Kamiyama

    IPC: G06T7/00 ,  G06T17/20 ,  G06N3/08 ,  A61B5/103

    Abstract: Systems and methods for generating pressure maps of real-world objects using deep learning are disclosed. A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using a process which in some embodiments utilizes photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation deep learning network (DLN) based on the structured 3D model and the object parameters, where the pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.

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