Abstract: According to examples, an image capture device may include a lens array including lenses supported by a lens support structure, in which the lenses are arranged to capture light rays from multiple view-points. The image capture device may also include a sensor to capture light and convert the captured light into data that is used to form an image and a plurality of apertures positioned between the lenses and the sensor, in which the apertures are positioned with respect to the lenses and the sensor to allow rays of light that would have reached a certain reference location spaced from the sensor to pass through the plurality of apertures. An optimization algorithm may be applied to the collected sensor data to reproduce an image of a scene at a perspective that is intended to match a virtual eye position that is offset from the location of the sensor.

Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.

Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.

Abstract: A system for adjusting an interpupillary distance and an eye relief distance of a headset. The system can determine an interpupillary distance of a user and an eye relief distance of the user. The system can cause an adjustment assembly to adjust a first distance between the first lens and the second lens along a first axis based at least in part on the interpupillary distance and cause the adjustment assembly to adjust the optical assembly a second distance along a second axis that is orthogonal to the first axis based at least in part on the eye relief distance.

Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.