Google Inc.

Mira Leung

Neal Wadhwa

Onur Gonen Guleryuz

Jonathan Tilton Barron

Shahram Izadi

Adarsh Prakash Murthy Kowdle

Sean Ryan Francesco Fanello

Christoph Rhemann

Vladimir Tankovich

Ambrus Csaszar

Konstantine Nicholas John Tsotsos

Michael John Schoenberg

Mirko Schmidt

Sameh Khamis

Vivek Verma

Eric Turner

Ivan Dryanovski

Julien Valentin

Maksym Dzitsiuk

Oleg Veryovka

João Manuel Castro Afonso

Jose Pascoal

Geoffrey Landskov

Steve Perry

Fares Alhassen

Abe Stephens

Implementations described herein relate to a computer-implemented method that includes capturing image data using one or more cameras, wherein the image data includes a primary image and associated depth values. The method further includes encoding the image data in an image format. The encoded image data includes the primary image encoded in the image format and image metadata that includes a device element that includes a profile element indicative of an image type and a first camera element, wherein the first camera element includes an image element and a depth map based on the depth values. The method further includes, after the encoding, storing the image data in a file container based on the image format. The method further includes causing the primary image to be displayed.

Techniques to capture and edit dynamic depth images

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

A method for embedding metadata into images and/or videos for AR experience is described. In one example implementation, the method may include generating a first image/video including an environment captured by a device and a virtually-rendered augmented reality (AR) object composited with the environment. The first image/video may be embedded with a first metadata. The method may further include generating a second image/video by modifying the first image/video. The second image/video may be embedded with a second metadata. The second metadata is generated based on the first metadata.

Embedding metadata into images and videos for augmented reality experience

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Seattle, WA, United States of America

Mira Leung