Fyusion, Inc.

Pavel Hanchar

Stefan Johannes Josef Holzer

Radu Bogdan Rusu

Abhishek Kar

Matteo Munaro

Krunal Ketan Chande

Aidas Liaudanskas

Martin Markus Hubert Wawro

Martin Saelzle

Chris Beall

Rodrigo Ortiz-Cayon

Santi Arano

Milos Vlaski

Wook Yeon Hwang

Johan Nordin

Nick Stetco

Matthias Reso

Julius Santiago

Stephen David Miller

Alexander Jay Bruen Trevor

George Haber

Pantelis Kalogiros

Mike Penz

Shuichi Tsutsumi

Nico Gregor Sebastian Blodow

Ioannis Spanos

Jai Chaudhry

Blake McConnell

Raul Dronca

Ashley Wakefield

Josh Faust

Santiago Arano Perez

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 via a camera at a mobile computing device at different viewpoints. The images may be used to identify components of the object and to identify damage estimates estimating damage to some or all of the components. Capture coverage levels corresponding with the components may be determined, and then recording guidance may be provided for capturing additional images to increase the capture coverage levels.

Multi-view visual data damage detection

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

Damage to an object such as a vehicle may be detected and presented based at least in part on image data. In some configurations, image data may be detected by causing the object to pass through a gate or portal on which cameras are located. Alternatively, or additionally, image data may be selected by a user operating a camera and moving around the object. The cameras may capture image data, which may be combined and analyzed to detect damage. Some or all of the image data and/or analysis of the image data may be presented in a viewer, which may allow a user to perform actions such as navigating around the object in a virtual environment, identifying and viewing areas of the object where damage has been detected, and accessing the results of the analysis.

Multi-view interactive digital media representation viewer

Images of an undercarriage of a vehicle may be captured via one or more cameras. A point cloud may be determined based on the images. The point cloud may includes points positioned in a virtual three-dimensional space. A stitched image may be determined based on the point cloud by projecting the point cloud onto a virtual camera view. The stitched image may be stored on a storage device.

Vehicle undercarriage imaging

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

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

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Minsk, Belarus

Pavel Hanchar