Invention Grant
- Patent Title: Using deep learning and structure-from-motion techniques to generate 3D point clouds from 2D data
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Application No.: US18355336Application Date: 2023-07-19
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Publication No.: US12223670B2Publication Date: 2025-02-11
- Inventor: Ryan Knuffman , Jeremy Carnahan
- Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- Applicant Address: US IL Bloomington
- Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- Current Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- Current Assignee Address: US IL Bloomington
- Agency: MARSHALL, GERSTEIN & BORUN LLP
- Main IPC: G06T7/579
- 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.
Public/Granted literature
- US20230360244A1 Using Deep Learning and Structure-From-Motion Techniques to Generate 3D Point Clouds From 2D Data Public/Granted day:2023-11-09
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