Fyusion, Inc.

Abhishek Kar

Stefan Johannes Josef Holzer

Radu Bogdan Rusu

Pavel Hanchar

Matteo Munaro

Alexander Jay Bruen Trevor

Krunal Ketan Chande

Pantelis Kalogiros

Chris Beall

Ioannis Spanos

Rodrigo Ortiz Cayon

Santi Arano

Ben Mildenhall

Yuheng Ren

Martin Markus Hubert Wawro

Aidas Liaudanskas

Matthias Reso

Martin Josef Nikolaus Saelzle

Stephen David Miller

George Haber

Martin Saelzle

Mike Penz

Shuichi Tsutsumi

Julius Santiago

Nico Gregor Sebastian Blodow

Rodrigo Ortiz-Cayon

Michelle Jung-Ah Ho

Jai Chaudhry

Luke Parham

Santiago Arano Perez

Ashley Wakefield

Josh Faust

Provided are mechanisms and processes for scene-aware selection of filters and effects for visual digital media content. In one example, a digital media item is analyzed with a processor to identify one or more characteristics associated with the digital media item, where the characteristics include a physical object represented in the digital media item. Based on the identified characteristics, a digital media modification is selected from a plurality of digital media modifications for application to the digital media item. The digital media modification may then be provided for presentation in a user interface for selection by a user.

Scene-aware selection of filters and effects for visual digital media content

A background scenery portion may be identified in each of a plurality of image sets of an object, where each image set includes images captured simultaneously from different cameras. A correspondence between the image sets may determined, where the correspondence tracks control points associated with the object and present in multiple images. A multi-view interactive digital media representation of the object that is navigable in one or more dimensions and that includes the image sets may be generated and stored.

Mobile multi-camera multi-view capture

Images of an object may be captured at a computing device. Each of the images may be captured from a respective viewpoint based on image capture configuration information identifying one or more parameter values. A multiview image digital media representation of the object may be generated that includes some or all of the images of the object and that is navigable in one or more dimensions.

Damage detection from multi-view visual data

A tag characterizing a portion of a multi-view interactive digital media representation (MVIDMR) may be determined by applying a grammar to natural language data. The MVIDMR may include images of an object and may be navigable in one or more dimensions. An object model location for the tag identifying a location within a three-dimensional object model may be determined by applying the grammar to the natural language data. The tag may then be applied to the MVIDMR by associating it with two or more of the images at positions determined based on the object model location.

Natural language understanding for visual tagging

The pose of an object may be estimated based on fiducial points identified in a visual representation of the object. Each fiducial point may correspond with a component of the object, and may be associated with a first location in an image of the object and a second location in a 3D coordinate pace. A 3D skeleton of the object may be determined by connecting the locations in the 3D space, and the object's pose may be determined based on the 3D skeleton.

Object pose estimation in visual data

A plurality of images may be analyzed to determine an object model. The object model may have a plurality of components, and each of the images may correspond with one or more of the components. Component condition information may be determined for one or more of the components based on the images. The component condition information may indicate damage incurred by the object portion corresponding with the component.

A multi-view interactive digital media representation (MVIDMR) of an object can be generated from live images of an object captured from a camera. Selectable tags can be placed at locations on the object in the MVIDMR. When the selectable tags are selected, media content can be output which shows details of the object at location where the selectable tag is placed. A machine learning algorithm can be used to automatically recognize landmarks on the object in the frames of the MVIDMR and a structure from motion calculation can be used to determine 3-D positions associated with the landmarks. A 3-D skeleton associated with the object can be assembled from the 3-D positions and projected into the frames associated with the MVIDMR. The 3-D skeleton can be used to determine the selectable tag locations in the frames of the MVIDMR of the object.

Method and apparatus for 3-D auto tagging

Provided are mechanisms and processes for performing visual search using multi-view digital media representations, such as surround views. In one example, a process includes receiving a visual search query that includes a surround view of an object to be searched, where the surround view includes spatial information, scale information, and different viewpoint images of the object. The surround view is compared to stored surround views by comparing spatial information and scale information of the surround view to spatial information and scale information of the stored surround views. A correspondence measure is then generated indicating the degree of similarity between the surround view and a possible match. At least one search result is then transmitted with a corresponding image in response to the visual search query.

Visual search using multi-view interactive digital media representations

According to various embodiments, component information may be identified for each input image of an object. The component information may indicate a portion of the input image in which a particular component of the object is depicted. A viewpoint may be determined for each input image that indicates a camera pose for the input image relative to the object. A three-dimensional skeleton of the object may be determined based on the viewpoints and the component information. A multi-view panel corresponding to the designated component of the object that is navigable in three dimensions and that the portions of the input images in which the designated component of the object is depicted may be stored on a storage device.

Extraction of standardized images from a single view or multi-view capture

One or more images of an object, each from a respective viewpoint, may be captured at a camera at a mobile computing device. The images may be compared to reference data to identify a difference between the images and the reference data. Image capture guidance may be provided on a display screen for capturing another one or more images of the object that includes the identified difference.

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Berkeley, CA, United States of America

Abhishek Kar