Abstract: A method and/or system for automated estimation of 3D orientation of a physical asset using deep learning models and computer vision algorithms, according to one or more embodiments. The system may be configured to receive images of the physical asset and camera orientation data as input, use deep learning neural network models to isolate the physical assets across the images, track each physical asset instance throughout the images and derive a 3D point cloud model of each asset by projecting binary masks of the asset contours from different view-points. The 3D point cloud model is further processed and supplemented with camera orientation data to estimate the 3D orientation of one or more assets present in the images.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data, and defining a first tile using the eye tracking data. The first tile has dimensions determined using the eye tracking data. The method also includes defining multiple additional tiles to fill a surrounding area of the display. The method also includes providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: An optical assembly includes an optically transparent substrate and a first lens removably coupled with the optically transparent substrate by one or more adhesive layers. The one or more adhesive layers are positioned between the optically transparent substrate and the first lens. Also disclosed are an eye-tracker and a display device, each of which includes the optical assembly.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data, and defining a first tile using the eye tracking data. The first tile has dimensions determined using the eye tracking data. The method also includes defining multiple additional tiles to fill a surrounding area of the display. The method also includes providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data, and defining a first tile using the eye tracking data. The first tile has dimensions determined using the eye tracking data. The method also includes defining multiple additional tiles to fill a surrounding area of the display. The method also includes providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: An optical assembly includes an optically transparent substrate and a first lens removably coupled with the optically transparent substrate by one or more adhesive layers. The one or more adhesive layers are positioned between the optically transparent substrate and the first lens. Also disclosed are an eye-tracker and a display device, each of which includes the optical assembly.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data, and defining a first tile using the eye tracking data. The first tile has dimensions determined using the eye tracking data. The method also includes defining multiple additional tiles to fill a surrounding area of the display. The method also includes providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data and determining a gaze location on the display or a gaze vector using the eye tracking data. The method can also include defining a first tile using the gaze location on the display or the gaze vector. The first tile may have a height and a width, the height and width being determined using the eye tracking data. The method can further include defining multiple additional tiles to fill an entire area of the display. The method can also include providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: The disclosed computer-implemented method may include identifying a region within a user's eye gaze and calculating a ranking for the identified region within the user's eye gaze. The ranking may indicate the user's level of interest in the identified region. The method may then determine how the identified region is to be presented according to the calculated ranking and present the identified region in the determined manner according to the calculated ranking. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems and methods for virtual interest segmentation may include (1) performing a semantic segmentation of an image of a user's environment, captured by an artificial reality (AR) device being worn by the user, to identify objects within the user's environment, (2) in addition to performing the semantic segmentation, performing an interest segmentation of the image to determine a personal interest that the user may have in a particular object identified via the semantic segmentation, (3) creating virtual content relating to the particular object based on the user's personal interest in the particular object, and (4) displaying the virtual content within a display element of the AR device. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method for providing imagery to a user on a display includes receiving eye tracking data and determining a gaze location on the display or a gaze vector using the eye tracking data. The method can also include defining a first tile using the gaze location on the display or the gaze vector. The first tile may have a height and a width, the height and width being determined using the eye tracking data. The method can further include defining multiple additional tiles to fill an entire area of the display. The method can also include providing a portion of an image using the first tile at a first image quality and providing another portion of the image at a second image quality using at least one of the multiple additional tiles.

Abstract: Systems and methods for enabling gaze-based virtual content control may include (1) displaying an artificial scene with one or more virtual elements to a user wearing a head-mounted display system, (2) identifying the user's eye gaze based on gazing data collected by one or more sensors in the head-mounted display system, (3) determining that the user's eye gaze is focused on a specific virtual element, and (4) in response to determining that the user's eye gaze is focused on the specific virtual element, increasing the specific virtual element's visibility to the user. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one embodiment, a computing system may determine that a performance metric of an eye tracking system is below a performance threshold. The eye tracking system may be associated with a head-mounted display worn by a user. In response to the determination that the performance metric is below the performance threshold, the system may identify one or more contents being displayed by the head-mounted display. The system may access one or more properties associated with the one or more contents. The system may predict a vergence distance of the user based at least on the one or more properties associated with the one or more display contents. The system may adjust one or more configurations based on the predicted vergence distance of the user.

Abstract: The disclosed computer-implemented method may include (i) determining, using an eye-tracking system, an orientation of at least one eye of a user, (ii) identifying, based at least in part on the orientation of the user's eye, a point of interest within a field of view of the user, (iii) determining that the point of interest is a candidate for tagging, and (iv) performing, in response to determining that the point of interest is the candidate for tagging, a tagging action that facilitates tagging of the point of interest. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include: identifying, using an eye-tracking system, an object within a scene viewed by a user; identifying, within a database of object interaction commands, a subset of commands that apply to the object viewed by the user; and presenting, to the user, the subset of commands that apply to the object. Various other methods, systems, devices, and computer-readable media are also disclosed.

Abstract: In one embodiment, the artificial reality system determines that a performance metric of an eye tracking system is below a first performance threshold. The eye tracking system is associated with a head-mounted display worn by a user. The artificial reality system receives first inputs associated with the body of a user and determines a region that the user is looking at within a field of view of a head-mounted display based on the received first inputs. The system determines a vergence distance of the user based at least on the first inputs associated with the body of the user, the region that the user is looking at, and locations of one or more objects in a scene displayed by the head-mounted display. The system adjusts one or more configurations of the head-mounted display based on the determined vergence distance of the user.