Google Inc.

Maksym Dzitsiuk

Ruofei Du

Onur Gonen Guleryuz

Jonathan Tilton Barron

Shahram Izadi

Neal Wadhwa

Adarsh Prakash Murthy Kowdle

Sean Ryan Francesco Fanello

Vladimir Tankovich

Christoph Rhemann

Ambrus Csaszar

Konstantine Nicholas John Tsotsos

Michael John Schoenberg

Mirko Schmidt

Sameh Khamis

Eric Turner

Vivek Verma

Julien Valentin

Ivan Dryanovski

Mira Leung

Jose Pascoal

João Manuel Castro Afonso

A method including determining a gaze direction of a user of a wearable device, capturing an image using a forward-looking camera of the wearable device, detecting a surroundings of the user based on the image, determining whether or not the user is distracted based on the gaze direction and the surroundings, and in response to determining the user is distracted, causing an operation to be performed on the wearable device, the operation configured to cause the user to change the user's attention.

Attention redirection of a user of a wearable device

A head-mounted device, such as augmented reality glasses, can be configured to track screen time. The head-mounted device can advantageously track screen time across a variety of devices whether or not they are configured to such measurements. Various modes for tracking the screen time may be used based on accuracy, power consumption, and privacy. The head-mounted device may track in a mode based on a battery level, a device setting or a user input. The head-mounted device may accumulate screen times in a database and in some implementations, can link screen times with devices in the database based on device information gathered by the head-mounted device during the tracking process. The head-mounted device may be configured to generate and display an alert based on one or more rules about screen time.

Head-mounted device for tracking screen time

A handheld user device includes a monocular camera to capture a feed of images of a local scene and a processor to select, from the feed, a keyframe and perform, for a first image from the feed, stereo matching using the first image, the keyframe, and a relative pose based on a pose associated with the first image and a pose associated with the keyframe to generate a sparse disparity map representing disparities between the first image and the keyframe. The processor further is to determine a dense depth map from the disparity map using a bilateral solver algorithm, and process a viewfinder image generated from a second image of the feed with occlusion rendering based on the depth map to incorporate one or more virtual objects into the viewfinder image to generate an AR viewfinder image. Further, the processor is to provide the AR viewfinder image for display.

Depth from motion for augmented reality for handheld user devices

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San Francisco, CA, United States of America

Maksym Dzitsiuk