20230141639. METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR FACILITIES MANAGEMENT AND OPERATIONS simplified abstract (STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY)
Contents
- 1 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR FACILITIES MANAGEMENT AND OPERATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR FACILITIES MANAGEMENT AND OPERATIONS - 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 FACILITIES MANAGEMENT AND OPERATIONS
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 FACILITIES MANAGEMENT AND OPERATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230141639 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR FACILITIES MANAGEMENT AND OPERATIONS
Simplified Explanation
The abstract describes a method, computing system, and computer-readable medium for using a trained generative adversarial network (GAN) to improve vehicle orientation and navigation. The method involves loading a semantically-segmented 3D point cloud into a virtual reality simulation environment, processing the point cloud, and displaying an output with at least one attribute.
- The method uses a GAN to enhance vehicle orientation and navigation.
- A semantically-segmented 3D point cloud is loaded into a virtual reality simulation environment.
- The point cloud is processed to extract relevant information.
- The output includes at least one attribute that aids in vehicle orientation and navigation.
Potential Applications
This technology has potential applications in various fields, including:
- Autonomous vehicles: The improved orientation and navigation can benefit self-driving cars and other autonomous vehicles.
- Robotics: Robots can utilize the enhanced orientation and navigation capabilities for better movement and operation.
- Augmented reality: The technology can be used to enhance the accuracy and realism of augmented reality experiences.
- Virtual reality: Virtual reality simulations can benefit from improved vehicle orientation and navigation.
Problems Solved
The technology addresses the following problems:
- Vehicle orientation: The method improves the accuracy and reliability of determining a vehicle's orientation in a virtual reality simulation.
- Navigation: By processing the 3D point cloud, the technology aids in determining the optimal path and navigation for vehicles.
- Semantic segmentation: The method enables the extraction of meaningful information from the point cloud by segmenting it based on semantic attributes.
Benefits
The use of this technology offers several benefits:
- Enhanced vehicle orientation: The method improves the accuracy of determining a vehicle's orientation, leading to more precise navigation.
- Improved navigation: By processing the point cloud, the technology helps vehicles navigate more efficiently and effectively.
- Realistic simulations: Virtual reality simulations can benefit from the enhanced orientation and navigation, providing a more realistic experience.
- Time and cost savings: The technology can potentially reduce the time and cost associated with developing and testing vehicle orientation and navigation systems.
Original Abstract Submitted
a method for using a trained generative adversarial network to improve vehicle orientation and navigation includes loading a semantically-segmented 3d point cloud into a virtual reality simulation environment; processing the 3d point cloud; and displaying an output including at least one attribute. 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: load a semantically-segmented 3d point cloud into a virtual reality simulation environment; process the 3d point cloud; and display an output including at least one attribute. a non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to: load a semantically-segmented 3d point cloud into a virtual reality simulation environment; process the 3d point cloud; and display an output including at least one attribute.
