Fyusion, Inc.

Aidas Liaudanskas

Stefan Johannes Josef Holzer

Matteo Munaro

Pavel Hanchar

Krunal Ketan Chande

Rodrigo Ortiz-Cayon

Radu Bogdan Rusu

Alexander Jay Bruen Trevor

Martin Saelzle

Martin Markus Hubert Wawro

Milos Vlaski

Wook Yeon Hwang

Johan Nordin

Nick Stetco

Abhishek Kar

Pantelis Kalogiros

Matthias Reso

Julius Santiago

Ioannis Spanos

Santi Arano

Srinivas Rao

Blake McConnell

Nishant Rai

Raul Dronca

One or more two-dimensional images of a three-dimensional object may be analyzed to estimate a three-dimensional mesh representing the object and a mapping of the two-dimensional images to the three-dimensional mesh. Initially, a correspondence may be determined between the images and a UV representation of a three-dimensional template mesh by training a neural network. Then, the three-dimensional template mesh may be deformed to determine the representation of the object. The process may involve a reprojection loss cycle in which points from the images are mapped onto the UV representation, then onto the three-dimensional template mesh, and then back onto the two-dimensional images.

Weak multi-view supervision for surface mapping estimation

A live camera feed may be analyzed to determine the identify of an object, and augmented reality overlay data may be determined based on that identity. The overlay data may include one or more tags that are each associated with a respective location on the object. The live camera feed may be presented on a display screen with the tags being positioned as the respective location.

Live in-camera overlays

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

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

Pixels in a visual representation of an object that includes one or more perspective view images may be mapped to a standard view of the object. Based on the mapping, a portion of the object captured in the visual representation of the object may be identified. A user interface on a display device may indicate the identified object portion.

Object capture coverage evaluation

Images of an object may be analyzed to determine individual damage maps of the object. Each damage map may represent damage to an object depicted in one of the images. The damage may be represented in a standard view of the object. An aggregated damage map for the object may be determined based on the individual damage maps.

Object damage aggregation

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San Francisco, CA, United States of America

Aidas Liaudanskas