Google Inc.

Julien Valentin

Vladimir Tankovich

Michael John Schoenberg

Shahram Izadi

Christoph Rhemann

Sameh Khamis

Sean Ryan Francesco Fanello

Sean Ryan Fanello

Yinda Zhang

Thomas Allen Funkhouser

Adarsh Kowdle

Onur Gonen Guleryuz

Jonathan Tilton Barron

Neal Wadhwa

Adarsh Prakash Murthy Kowdle

Ambrus Csaszar

Konstantine Nicholas John Tsotsos

Mirko Schmidt

Eric Turner

Vivek Verma

Ivan Dryanovski

Maksym Dzitsiuk

Mira Leung

João Manuel Castro Afonso

Jose Pascoal

An electronic device estimates a depth map of an environment based on matching reduced-resolution stereo depth images captured by depth cameras to generate a coarse disparity (depth) map. The electronic device downsamples depth images captured by the depth cameras and matches sections of the reduced-resolution images to each other to generate a coarse depth map. The electronic device upsamples the coarse depth map to a higher resolution and refines the upsampled depth map to generate a high-resolution depth map to support location-based functionality.

Active stereo depth prediction based on coarse matching

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 includes capturing a first image and a second image of a scene using at least one imaging camera of an imaging system. The first image and the second image form a stereo image pair and each comprises a plurality of pixels. Each of the plurality of pixels in the second image is initialized with a disparity hypothesis. Matching costs of the disparity hypothesis for each of the plurality of pixels in the second image are recursively determined, from an image tile of a smaller pixel size to an image tile of a larger pixel size, to generate an initial tiled disparity map including a plurality of image tiles. After refining the disparity value estimate of each image tile and including a slant hypothesis, a final disparity estimate for each pixel of the image is generated.

Hierarchical disparity hypothesis generation with slanted support windows

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Mountain View, CA, United States of America

Julien Valentin