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    SYSTEMS AND METHODS FOR LABELING 3D MODELS USING VIRTUAL REALITY AND AUGMENTED REALITY
    12.
    发明申请
    SYSTEMS AND METHODS FOR LABELING 3D MODELS USING VIRTUAL REALITY AND AUGMENTED REALITY 有权

    公开(公告)号：US20220092854A1

    公开(公告)日：2022-03-24

    申请号：US17543105

    申请日：2021-12-06

    Applicant: State Farm Mutual Automobile Insurance Company

    Inventor： Bryan Nussbaum ,  Jeremy Carnahan ,  Ryan Knuffman

    IPC: G06T17/20 ,  G06T19/00 ,  G06K9/00 ,  G06F16/58 ,  H04W4/021

    Abstract: A computer-implemented method for labeling a three-dimensional (3D) model using virtual reality (VR) techniques implemented by a computer system including a processor is provided herein. The method may include (i) receiving a 3D model including an environmental feature that is unlabeled, (ii) displaying, through a VR device in communication with the processor, a VR environment to a user representing the 3D model, (iii) prompting a user to input labeling data for the environmental feature displayed within the VR environment of the VR device by prompting the user to select the environmental feature through user interaction with the VR device, and input labeling data for the environmental feature, wherein the labeling data identifies the environmental feature, and (iv) generating a labeled 3D model by embedding the labeling data associated with the selected environmental feature into the 3D model.

    Methods and systems for using trained generative adversarial networks to impute 3D data for construction and urban planning
    14.
    发明授权
    Methods and systems for using trained generative adversarial networks to impute 3D data for construction and urban planning 有权

    公开(公告)号：US12243199B2

    公开(公告)日：2025-03-04

    申请号：US18618673

    申请日：2024-03-27

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Ryan Knuffman

    IPC: G06T5/77 ,  G06N3/045 ,  G06N3/088 ,  G06T7/579

    Abstract: A computer-implemented method for using a trained generative adversarial network to improve construction and urban planning includes receiving a semantically-segmented point cloud corresponding to a construction site; determining a volumetric soil measurement; and generating a cost estimate. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate. A non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate.

    Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data
    16.
    发明授权
    Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data 有权

    公开(公告)号：US12223670B2

    公开(公告)日：2025-02-11

    申请号：US18355336

    申请日：2023-07-19

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Ryan Knuffman ,  Jeremy Carnahan

    IPC: G06T7/579 ,  G06T15/20

    Abstract: A server includes a processor and a memory storing instructions that, when executed by the processor, cause the server to receive two-dimensional (2D) images, analyze the images using a trained deep network to generate points, process the labeled points to identify tie points, and combine the 2D dimensional images into a three-dimensional (3D) point cloud using structure-from-motion. A method for generating a semantically-segmented 3D point cloud from 2D data includes receiving 2D images, analyzing the images using a trained deep network to generate labeled points, processing the points to identify tie points, and combining the 2D images into a 3D point cloud using structure-from-motion. A non-transitory computer readable storage medium stores executable instructions that, when executed by a processor, cause a computer to receive 2D images, analyze the images using a trained deep network to generate labeled points, process the points to identify and combine tie points using structure-from-motion.

    METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING
    20.
    发明申请
    METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING 有权

    公开(公告)号：US20230139702A1

    公开(公告)日：2023-05-04

    申请号：US18091254

    申请日：2022-12-29

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Ryan Knuffman

    IPC: G06T5/00 ,  G06T7/579 ,  G06N3/088 ,  G06N3/045

    Abstract: A computer-implemented method for using a trained generative adversarial network to improve construction and urban planning includes receiving a semantically-segmented point cloud corresponding to a construction site; determining a volumetric soil measurement; and generating a cost estimate. A computing system for using a trained generative adversarial network to improve vehicle orientation and navigation includes one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate. A non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a semantically-segmented point cloud corresponding to a construction site; determine a volumetric soil measurement; and generate a cost estimate.

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