Abstract: The invention provides a real-time verification system for operators of aircraft, vehicles, and other modes of transportation for safety and security before commencing travel. The system comprises a processing device installed in the preferred mode of transportation, and includes a processor, memory, and an AI model. The processor integrates modules for receiving biometric data (fingerprint, facial, or retinal scans), verifying identities against a cloud database of operator data and the Terrorist Screening Database (TSDB), and communicating results to Air Traffic Control (ATC). The AI model performs advanced analytics, monitoring verification data against comprehensive databases, and predicting potential issues by analyzing historical trends. It generates detailed reports and alerts for ATC and other authorities, contributing to both immediate safety and long-term improvements.

Abstract: Systems and methods for enhanced computer vision capabilities, particularly including depth synthesis, which may be applicable to autonomous vehicle operation are described. A vehicle may be equipped with a geometric scene representation (GSR) architecture for synthesizing depth views at arbitrary viewpoints. The GSR architecture synthesizes depth views enable advanced functions, including depth interpolation and depth extrapolation. The GSR architecture implements functions (i.e., depth interpolation, depth extrapolation) that are useful for various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations. For example, a vehicle includes a processor device synthesizing depth views at multiple viewpoints, where the multiple viewpoints are from image data of a surrounding environment for the vehicle.

Abstract: Systems and methods for enhanced computer vision capabilities, particularly including depth synthesis, which may be applicable to autonomous vehicle operation are described. A vehicle may be equipped with a geometric scene representation (GSR) architecture for synthesizing depth views at arbitrary viewpoints. The GSR architecture synthesizes depth views enable advanced functions, including depth interpolation and depth extrapolation. The GSR architecture implements functions (i.e., depth interpolation, depth extrapolation) that are useful for various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations. For example, a vehicle includes a processor device synthesizing depth views at multiple viewpoints, where the multiple viewpoints are from image data of a surrounding environment for the vehicle.

Abstract: A method for fusing neural radiance fields (NeRFs) is described. The method includes re-rendering a first NeRF and a second NeRF at different viewpoints to form synthesized images from the first NeRF and the second NeRF. The method also includes inferring a transformation between a re-rendered first NeRF and a re-rendered second NeRF based on the synthesized images from the first NeRF and the second NeRF. The method further includes blending the re-rendered first NeRF and the re-rendered second NeRF based on the inferred transformation to fuse the first NeRF and the second NeRF.

Abstract: Systems and methods for enhanced computer vision capabilities, particularly including depth synthesis, which may be applicable to autonomous vehicle operation are described. A vehicle may be equipped with a geometric scene representation (GSR) architecture for synthesizing depth views at arbitrary viewpoints. The GSR architecture synthesizes depth views enable advanced functions, including depth interpolation and depth extrapolation. The GSR architecture implements functions (i.e., depth interpolation, depth extrapolation) that are useful for various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations. For example, a vehicle includes a processor device synthesizing depth views at multiple viewpoints, where the multiple viewpoints are from image data of a surrounding environment for the vehicle.