Google Inc.

Other

Nicholas Butko

Erik Murphy-Chutorian

Aravind Krishnaswamy

Matthew Stephen Steiner

Vivek Kwatra

David Cohen

Matthias Grundmann

Shengyang Dai

John Patrick Flynn

Troy Chinen

Timothy Sepkoski St Clair

Florian Kriener

John Spiegel

Falk Sticken

Tom Binder

Steven Vandebogart

Nikolaos Trogkanis

Ingo Wehmeyer

Scott Ettinger

Evan Rapoport

Dave Cohen

Josh T Wills

Emily Chang

Alvin Portillo

Rigel Benton

Dat Chu

Brian Matiash

Scott Pollack

Implementations generally relate to providing augmented reality in a web browser. In one implementation, a method includes capturing images of a physical scene with a camera of a device. The method further includes determining motion of the camera using six degrees of freedom (6DoF) markerless tracking. The method further includes overlaying virtual three-dimensional (3D) content onto a depicted physical scene in the images, resulting in augmented reality (AR) images. The method further includes rendering the AR images in a browser of the device.

Providing augmented reality in a web browser

Implementations generally relate to determining camera motion. In one implementation, a method includes capturing a first image and a second image of a physical scene with a camera in respective first and second positions. The method further includes determining first and second image points from the respective first and second image points. The method further includes determining first and second directions of gravity relative to the camera in the respective first and second positions. The method further includes determining a motion of the camera between the capturing of the first image and the capturing of the second image, wherein the determining of the motion of the camera is based at least in part on the first image points, the second image points, the first direction of gravity, and the second direction of gravity.

Camera motion estimation

Implementations generally relate to providing augmented reality in a web browser. In one implementation, a method includes capturing images of a physical scene with a camera of a device. The method further includes determining motion of the camera using six degrees of freedom (6DoF) marker-based tracking. The method further includes determining positions of markers relative to the camera using 6DoF marker-based tracking. The method further includes overlaying virtual content onto a depicted physical scene in the images, resulting in augmented reality (AR) images. The method further includes rendering the AR images in a browser of the device.

Providing augmented reality target images in a web browser

Implementations generally relate to localizing and mapping. In one implementation, a method includes determining one or more map points in a point cloud space, where the point cloud space corresponds to a real physical environment. The method further includes determining movement information of one or more of the map points, where the determining of the movement information is performed by a neural network. The method further includes determining a self-position in the point cloud space based on the determined movement information.

Localizing and mapping platform

Various embodiments for event-based information sharing are disclosed. In one example embodiment, an event definition describing an event is determined. An event geofence associated with a location of the event is generated and content is shared between invitees attending the event and located within an area of the event geofence.

Event-based content sharing using geofencing

Implementations generally relate to generating compositional media content. In some implementations, a method includes receiving a plurality of photos from a user, and determining one or more composition types from the photos. The method also includes generating compositions from the selected photos based on the one or more determined composition types. The method also includes providing the one or more generated compositions to the user.

Generating compositions

Implementations relate to detecting spherical images. In some implementations, a computer-executed method includes obtaining an image, examining at least one characteristic of the image, and determining that the image is a spherical image based on the at least one examined characteristic. The method modifies metadata associated with the image to designate the image for spherical display.

Detecting spherical images

In some implementations, a method provides image candidates for a user. The method can include applying a plurality of different image adjustment types to an image to produce a plurality of different adjusted images. A score is determined for each of the adjusted images, where each score is based on previous selections by a plurality of persons of other images having one or more characteristics similar to the adjusted images. The method determines one or more candidate images for presentation to a particular user, where each candidate image is obtained from one of the different image adjustment types. The candidate images are determined based on the scores of the adjustment types.

Providing image candidates based on diverse adjustments to an image

Implementations generally relate to generating photo animations. In some implementations, a method includes receives a plurality of photos from a user. The method also includes selecting photos from the plurality of photos that meet one or more predetermined similarity criteria. The method also includes generating an animation using the selected photos.

Generating photo animations

In some implementations, a method rotates images based on image content to correct image orientation. In some implementations, a method includes obtaining one or more identifications of content depicted in an image and determining a current orientation of the content depicted in the image. The current orientation is determined based on the one or more identifications of the content. An amount of rotation for the image is determined that orients the content closer to a predetermined reference orientation than to the current orientation. The image is rotated by the determined amount.

Rotation of an image based on image content to correct image orientation

In some implementations, a method provides color corrections based on multiple images. In some implementations, a method includes determining one or more characteristics of each of a plurality of source images and determining one or more similarities between the one or more characteristics of different source images. The source images are grouped into one or more groups of one or more target images based on the determined similarities. The method determines and applies one or more color corrections to the one or more target images in at least one of the groups.

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Mountain View, CA, United States of America

Nicholas Butko