Microsoft Technology Licensing, LLC

Google Inc.

University of Washington

Keith Noah Snavely

Steven Maxwell Seitz

Richard S Szeliski

John Patrick Flynn

Ivan Neulander

Forrester H Cole

Andrew C Gallagher

Richard Tucker

James William Philbin

Zhengqi Li

Pierre Georgel

Tsung-Lin Yang

Bryce Evans

Yihui Xie

Qianqian Wang

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for rendering a new image that depicts a scene from a perspective of a camera at a new camera viewpoint at a given time point in a video.

Neural dynamic image-based rendering

A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Deepstereo: learning to predict new views from real world imagery

Aspects of this disclosure relate to generating a composite image of an image of and another image that has a wider field of view. After an image is selected, the visual features in the image may be identified. Several images, such as panoramas, which have wider fields of view than an image captured by a camera, may be selected according to a comparison of the visual features in the image and the visual features of the larger images. The image may be aligned with each of the larger images, and at least one of these smaller-larger image pairs may be generated as a composite image.

Expanding the field of view of photograph

Aspects of the disclosure relate to identifying visited travel destinations from a set of digital images associated with users of a social networking system. For example, one or more computing devices provide access to an individual user's account, including the individual user and other users affiliated with the individual user via the social networking system. One or more digital images are received from a computing device associated with the individual user and from one or more second computing devices associated with the other users of the social networking system. From each digital image, a geo-location is determined for each digital image. The one or more computing devices display each geo-located image on a map at a position corresponding to the determined geo-location for the geo-located image.

Discovering visited travel destinations from a set of digital images

Over the past few years there has been a dramatic proliferation of digital cameras, and it has become increasingly easy to share large numbers of photographs with many other people. These trends have contributed to the availability of large databases of photographs. Effectively organizing, browsing, and visualizing such .seas. of images, as well as finding a particular image, can be difficult tasks. In this paper, we demonstrate that knowledge of where images were taken and where they were pointed makes it possible to visualize large sets of photographs in powerful, intuitive new ways. We present and evaluate a set of novel tools that use location and orientation information, derived semi-automatically using structure from motion, to enhance the experience of exploring such large collections of images.

Navigating images using image based geometric alignment and object based controls

Growing community of inventors

Seattle, WA, United States of America

Keith Noah Snavely