A method includes tagging, by at least one processor, one or more three-dimensional assets with a unique identifier and storing the one or more three-dimensional assets in a database, creating, by the at least one processor, a three-dimensional model based on the one or more three-dimensional assets and loading the three-dimensional model in a simulator, generating, by the at least one processor, a two-dimensional image that is a representation of the three-dimensional model in the simulator, the two-dimensional image comprising metadata that includes each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image, and assigning, by the at least one processor, the two-dimensional image with a unique identifier and storing each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image in metadata for the two-dimensional image.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

In some embodiments, the present specification describes methods for displaying a three-dimensional image of an isolated threat object or region of interest with a single touch or click and providing spatial and contextual information relative to the object, while also executing a view dependent virtual cut-away or rendering occluding portions of the reconstructed image data as transparent. In some embodiments, the method includes allowing operators to associate audio comments with a scan image of an object. In some embodiments, the method also includes highlighting a plurality of voxels, which are indicative of at least one potential threat item, in a mask having a plurality of variable color intensities, where the intensities may be varied based on the potential threat items.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

An orthogonal or multi-dimensional fabric interface is described herein to select and present content to a user. A location is determined and time is selected for the user. The multi-dimensional fabric is accessed for content based the determined location and the selected time. If content is stored in the multi-dimensional fabric at the determined location and the selected time, the user is notified of the content, which can then be presented to the user.

An orthogonal or multi-dimensional fabric interface is described herein to select and present content to a user. A location is determined and time is selected for the user. The multi-dimensional fabric is accessed for content based the determined location and the selected time. If content is stored in the multi-dimensional fabric at the determined location and the selected time, the user is notified of the content, which can then be presented to the user.

A system and method for tracking differences between a last state seen by a user and a current state is provided. A user views a graphical user interface (GUI) wind that displays one or more states. Should the user's focus shift from the GUI and return after one or more states displayed therein have been modified, the system highlights the change between the current state and the user's last seen state.

The invention pertains to methods and apparatus for compensating the rendering of virtual content in an augmented reality scene as a function of the white point, spectral distribution, color or other optical parameter of the real content in the scene.

The invention pertains to methods and apparatus for compensating the rendering of virtual content in an augmented reality scene as a function of the white point, spectral distribution, color or other optical parameter of the real content in the scene.