Dropbox, Inc.

Taaz, Inc.

University of California

Other

Adobe, Inc.

Photometria, Inc.

Yale University

Kriegman-belhumeur Vision Technologies, LLC

David J Kriegman

Peter Neil Belhumeur

Thomas R Berg

Nils Peter Welinder

Jongmin Baek

Ying Xiong

Simon Kozlov

Satya Prakash Mallick

Kevin Barnes

Kalyan Krishna Sunkavalli

Zexiang Xu

Bradley Neuberg

Aditi Jain

Sai Bi

Leonard Fink

Neeraj Kumar

Ravi Ramamoorthi

Walter Kim

David W Jacobs

Todd Zickler

Manveer Singh Chawla

Swapnil Zarekar

Karandeep Johar

Aaron Feldstein

Joe Nudell

Jenny Dong

Satya P Mallick

Jared Wilson

Luke Thompson

Robert Kajic

Hui Qiao

Douglas A Fidaleo

Yannick Hold-Geoffroy

Matthew William Flagg

Deepu John

Brent Foust

Jennifer Neuman

Margaret Rose Ciafardini Kirkland

Kristine Anna Dougherty

Vipanj B Patel

Athinodoros Georghiades

Miloš Hašan

The present disclosure is directed toward systems and methods to quickly and accurately identify boundaries of a displayed document in a live camera image feed, and provide a document boundary indicator within the live camera image feed. For example, systems and methods described herein utilize different display document detection processes in parallel to generate and provide a document boundary indicator that accurately corresponds with a displayed document within a live camera image feed. Thus, a user of the mobile computing device can easily see whether the document identification system has correctly identified the displayed document within the camera viewfinder feed.

Live document detection in a captured video stream

A scene reconstruction system renders images of a scene with high-quality geometry and appearance and supports view synthesis, relighting, and scene editing. Given a set of input images of a scene, the scene reconstruction system trains a network to learn a volume representation of the scene that includes separate geometry and reflectance parameters. Using the volume representation, the scene reconstruction system can render images of the scene under arbitrary viewing (view synthesis) and lighting (relighting) locations. Additionally, the scene reconstruction system can render images that change the reflectance of objects in the scene (scene editing).

Scene reconstruction using geometry and reflectance volume representation of scene

The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.

Enhancing documents portrayed in digital images

Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.

Generating enhanced three-dimensional object reconstruction models from sparse set of object images

The present disclosure relates to generating computer searchable text from digital images that depict documents utilizing an orientation neural network and/or text prediction neural network. For example, one or more embodiments detect digital images that depict documents, identify the orientation of the depicted documents, and generate computer searchable text from the depicted documents in the detected digital images. In particular, one or more embodiments train an orientation neural network to identify the orientation of a depicted document in a digital image. Additionally, one or more embodiments train a text prediction neural network to analyze a depicted document in a digital image to generate computer searchable text from the depicted document. By utilizing the identified orientation of the depicted document before analyzing the depicted document with a text prediction neural network, the disclosed systems can efficiently and accurately generate computer searchable text for a digital image that depicts a document.

Generating searchable text for documents portrayed in a repository of digital images utilizing orientation and text prediction neural networks

One or more embodiments of a digital content system allow a user to conveniently search and/or navigate through a collection of digital content items. In particular, a user can interact with a client device to search for and identify one or more digital content items within a collection of digital content items. For example, the digital content system may provide a photo from a collection of photos via a graphical user interface. The digital content system can receive a user input identifying a selection of one or more visual features within the photo. Based on the selected visual feature(s), the digital content system may identify photos from the collection of photos that include the identified visual feature(s) and provide access to the identified photos via the graphical user interface.

Navigating digital content using visual characteristics of the digital content

Techniques are provided for delayed processing for arm policy determination for content management system messaging, including, during a delayed processing window, receiving reward data for arm actions taken, where the arm actions were chosen based on a previous version of an arm choice policy, and the previous version of the arm choice policy was determined based on a previous set of reward data for a previous set of arm actions taken. When the delayed processing window has closed, a new arm choice policy is determined based at least in part on the action-reward data, and the previous set of reward data and/or the previous arm choice policy. After a request to choose an arm choice is received, a particular arm action to take is determined based on the new arm choice policy. This chosen arm is provided in response to the request.

Delayed processing for arm policy determination for content management system messaging

Growing community of inventors

San Diego, CA, United States of America

David J Kriegman