Google Inc.

Lytro, Inc.

Alex Song

Colvin Pitts

Chia-Kai Liang

Ariel Braunstein

Kurt Akeley

Mike Xiaoli Ma

Jonathan H Frank

Derek Pang

Nikhil Karnad

Julio Cesar Hernandez Zaragoza

Steve Cooper

Tim Milliron

Yusuke Yasui

Saeid Shahhosseini

Bipeng Zhang

Orin Green

A combined video of a scene may be generated for applications such as virtual reality or augmented reality. In one method, a data store may store video data with a first portion having a first importance metric, and a second portion having a second importance metric, denoting that viewing of the first portion is more likely and/or preferential to viewing of the second portion. The subset may be retrieved and used to generate viewpoint video from a virtual viewpoint corresponding to a viewer's viewpoint. The viewpoint video may be displayed on a display device. One of storing the video data, retrieving the subset, and using the subset to generate the viewpoint video may include, based on the difference between the first and second importance metrics, expediting and/or enhancing performance of the step for the first portion, relative to the second portion.

Adaptive control for immersive experience delivery

A combined video of a scene may be generated for applications such as virtual reality or augmented reality. In one method, a camera system may be oriented at a first orientation and used to capture first video of a first portion of the scene. The camera system may then be rotated to a second orientation and used to capture second video of a second portion of the scene that is offset from the first portion such that the first video and the second video each have an overlapping video portion depicting an overlapping portion of the scene in which the first portion and the second portion of the scene overlap with each other. The first and second portions may be combined together to generate the combined video, which may depict the first and second portions substantially without duplicative inclusion of the overlapping video portion.

Wedge-based light-field video capture

Depth information is determined for elements in a light field image, thus allowing for rapid display of visualization tools to communicate such depth information to a user. Depth of strong edges within the light field image is analyzed, providing improved reliability of depth information while reducing or minimizing the amount of computation involved in generating such information. Strong edges can be identified and analyzed by generating epipolar images, or EPIs, from the light field image. Local gradients are determined for pixels in the EPIs. The magnitude of the local gradient is used to determine a confidence as to whether depth can be reliably estimated from the gradient. The orientation of the gradient is used to determine the depth of a corresponding element of the scene. Suitable output is then generated based on the determined depths, for example to provide information and feedback to aid a user in capturing light-field images.

Depth determination for light field images

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San Jose, CA, United States of America

Alex Song