Abstract: The techniques described herein disclose a system that is configured to detect and track the three-dimensional pose of an object (e.g., a head-mounted display device) in a color image using an accessible three-dimensional model of the object. The system uses the three-dimensional pose of the object to repair pixel depth values associated with a region (e.g., a surface) of the object that is composed of material that absorbs light emitted by a time-of-flight depth sensor to determine depth. Consequently, a color-depth image (e.g., a Red-Green-Blue-Depth image or RGB-D image) can be produced that does not include dark holes on and around the region of the object that is composed of material that absorbs light emitted by the time-of-flight depth sensor.

Abstract: One disclosed example provides a head-mounted device configured to control a plurality of light sources of a handheld object and acquire image data comprising a sequence of environmental tracking exposures in which the plurality of light sources are controlled to have a lower integrated intensity and handheld object tracking exposures in which the plurality of light sources are controlled to have a higher integrated intensity. The instructions are further executable to detect, via an environmental tracking exposure, one or more features of the surrounding environment, determine a pose of the head-mounted device based upon the one or more features of the surrounding environment detected, detect via a handheld object tracking exposure the plurality of light sources of the handheld object, determine a pose of the handheld object relative to the head-mounted device based upon the plurality of light sources detected, and output the pose of the handheld object.

Abstract: A method for biometric verification includes obtaining a verification image and extracting a set of verification image features from the verification image. The method also includes processing the set of verification image features and a set of enrollment image features using a convolutional neural network to determine a metric. A determination may then be made about whether the verification image matches an enrollment image based on the metric.

Abstract: One disclosed example provides a head-mounted device configured to control a plurality of light sources of a handheld object and acquire image data comprising a sequence of environmental tracking exposures in which the plurality of light sources are controlled to have a lower integrated intensity and handheld object tracking exposures in which the plurality of light sources are controlled to have a higher integrated intensity. The instructions are further executable to detect, via an environmental tracking exposure, one or more features of the surrounding environment, determine a pose of the head-mounted device based upon the one or more features of the surrounding environment detected, detect via a handheld object tracking exposure the plurality of light sources of the handheld object, determine a pose of the handheld object relative to the head-mounted device based upon the plurality of light sources detected, and output the pose of the handheld object.

Abstract: Examples are disclosed that relate to tracking a pose of a handheld object used with a head-mounted display device. In one example, a method comprises: receiving image data from an image sensing system; detecting a plurality of feature points of the handheld object in a frame of the image data; receiving inertial measurement unit (IMU) data from an IMU of the handheld object; based on detecting the plurality of feature points and receiving the IMU data, determining a first pose of the handheld object; determining that at least a portion of the plurality of feature points is not detected in another frame of the image data; using the IMU data, updating the first pose of the handheld object to a second pose; and body-locking the second pose of the handheld object to a body location on a user wearing the head-mounted display device.

Abstract: One disclosed example provides a head-mounted device configured to control a plurality of light sources of a handheld object and acquire image data comprising a sequence of environmental tracking exposures in which the plurality of light sources are controlled to have a lower integrated intensity and handheld object tracking exposures in which the plurality of light sources are controlled to have a higher integrated intensity. The instructions are further executable to detect, via an environmental tracking exposure, one or more features of the surrounding environment, determine a pose of the head-mounted device based upon the one or more features of the surrounding environment detected, detect via a handheld object tracking exposure the plurality of light sources of the handheld object, determine a pose of the handheld object relative to the head-mounted device based upon the plurality of light sources detected, and output the pose of the handheld object.

Abstract: Examples are disclosed herein that relate to determining a pose of a handheld object. One example provides a computing system configured to determine a pose of a handheld object comprising a plurality of light sources by acquiring image data of a surrounding environment, detecting a subset of light sources of the plurality of light sources of the handheld object in the image data, and performing a search, without using previous pose data, to determine the pose of the handheld object relative to the computing system. The computing system is further configured to use the pose determined to perform a later search for an updated pose of the handheld object, and if the later search fails to find the updated pose, determine the updated pose by again performing the search without using previous pose data.

Abstract: One disclosed example provides a method for determining a pose of a handheld object in a surrounding environment. Optical pose data is stored in an image queue of a first filter. IMU data is received from an IMU of the handheld object and stored in an IMU queue of the first filter. Using at least a portion of the optical pose data and the IMU data, an initial pose of the handheld object is determined and outputted. The method determines that either the image queue or the IMU queue is empty. A second filter comprising the one empty queue and the other non-empty queue is instantiated as a copy of the first filter. Using the data from the non-empty queue in the second filter, the initial pose of the handheld object is updated to an updated pose, and the updated pose is outputted.

Abstract: Examples are disclosed herein related to tracking poses of a head-mounted display device that interfaces with handheld peripheral objects. One disclosed example provides a handheld object configured for providing user input to a head-mounted device, the handheld object including a body, a plurality of visible light sources arranged on the body in an arrangement trackable by a vision system of the head-mounted device, and a controller configured to control a brightness of one or more visible light sources of the plurality of visible light sources.

Abstract: One disclosed example provides a method for determining a pose of a handheld object in a surrounding environment. Optical pose data is stored in an image queue of a first filter. IMU data is received from an IMU of the handheld object and stored in an IMU queue of the first filter. Using at least a portion of the optical pose data and the IMU data, an initial pose of the handheld object is determined and outputted. The method determines that either the image queue or the IMU queue is empty. A second filter comprising the one empty queue and the other non-empty queue is instantiated as a copy of the first filter. Using the data from the non-empty queue in the second filter, the initial pose of the handheld object is updated to an updated pose, and the updated pose is outputted.

Abstract: Examples are disclosed herein that relate to determining a pose of a handheld object. One example provides a computing system configured to determine a pose of a handheld object comprising a plurality of light sources by acquiring image data of a surrounding environment, detecting a subset of light sources of the plurality of light sources of the handheld object in the image data, and performing a search, without using previous pose data, to determine the pose of the handheld object relative to the computing system. The computing system is further configured to use the pose determined to perform a later search for an updated pose of the handheld object, and if the later search fails to find the updated pose, determine the updated pose by again performing the search without using previous pose data.

Abstract: Examples are disclosed that relate to tracking a pose of a handheld object used with a head-mounted display device. In one example, a method comprises: receiving image data from an image sensing system; detecting a plurality of feature points of the handheld object in a frame of the image data; receiving inertial measurement unit (IMU) data from an IMU of the handheld object; based on detecting the plurality of feature points and receiving the IMU data, determining a first pose of the handheld object; determining that at least a portion of the plurality of feature points is not detected in another frame of the image data; using the IMU data, updating the first pose of the handheld object to a second pose; and body-locking the second pose of the handheld object to a body location on a user wearing the head-mounted display device.

Abstract: One disclosed example provides a head-mounted device configured to control a plurality of light sources of a handheld object and acquire image data comprising a sequence of environmental tracking exposures in which the plurality of light sources are controlled to have a lower integrated intensity and handheld object tracking exposures in which the plurality of light sources are controlled to have a higher integrated intensity. The instructions are further executable to detect, via an environmental tracking exposure, one or more features of the surrounding environment, determine a pose of the head-mounted device based upon the one or more features of the surrounding environment detected, detect via a handheld object tracking exposure the plurality of light sources of the handheld object, determine a pose of the handheld object relative to the head-mounted device based upon the plurality of light sources detected, and output the pose of the handheld object.

Abstract: Examples are disclosed herein related to tracking poses of a head-mounted display device that interfaces with handheld peripheral objects. One disclosed example provides a handheld object configured for providing user input to a head-mounted device, the handheld object including a body, a plurality of visible light sources arranged on the body in an arrangement trackable by a vision system of the head-mounted device, and a controller configured to control a brightness of one or more visible light sources of the plurality of visible light sources.