20230139702. METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING simplified abstract (STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY)
Contents
- 1 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING
Organization Name
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Inventor(s)
Ryan Knuffman of Danvers IL (US)
METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230139702 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR CONSTRUCTION AND URBAN PLANNING
Simplified Explanation
The patent application describes a computer-implemented method for using a trained generative adversarial network to improve construction and urban planning. It involves receiving a semantically-segmented point cloud representing a construction site, determining a volumetric soil measurement, and generating a cost estimate.
- The method uses a trained generative adversarial network to analyze and interpret a semantically-segmented point cloud of a construction site.
- It calculates the volumetric soil measurement, which provides information about the amount of soil present at the site.
- Based on the analysis and soil measurement, the method generates a cost estimate for the construction project.
Potential Applications
- Construction and urban planning: The technology can be used to improve the efficiency and accuracy of construction and urban planning projects by providing detailed analysis and cost estimates based on semantically-segmented point cloud data.
Problems Solved
- Inaccurate cost estimates: The technology addresses the problem of inaccurate cost estimates in construction projects by using a trained generative adversarial network to analyze the site and provide a more precise estimation.
- Lack of detailed analysis: The method solves the problem of limited analysis in construction and urban planning by utilizing semantically-segmented point cloud data to provide a comprehensive understanding of the site.
Benefits
- Improved accuracy: By using a trained generative adversarial network and volumetric soil measurement, the technology improves the accuracy of cost estimates for construction projects.
- Enhanced planning: The detailed analysis provided by the method allows for better planning and decision-making in construction and urban planning projects.
- Time and cost savings: The technology streamlines the estimation process, saving time and reducing costs associated with construction and urban planning projects.
Original Abstract Submitted
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.
