Abstract: Images of an object may be captured by cameras located at fixed locations in space as the object travels through the cameras' fields of view. A three-dimensional model of the object may be determined using the images. A portion of the object that has been damaged may be identified based on the three-dimensional model and the images. A damage map of the object illustrating the portion of the object that has been damaged may be generated.

Abstract: 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.

Abstract: Reference images of an object may be mapped to an object model to create a reference object model representation. Evaluation images of the object may also be mapped to the object model via the processor to create an evaluation object model representation. Object condition information may be determined by comparing the reference object model representation with the evaluation object model representation. The object condition information may indicate one or more differences between the reference object model representation and the evaluation object model representation. A graphical representation of the object model that includes the object condition information may be displayed on a display screen.

Abstract: Images may be captured from a plurality of cameras of an object moving along a path. Each of the cameras may be positioned at a respective identified location in three-dimensional space. Correspondence information for the plurality of images linking locations on different ones of the images may be determined. Linked locations may correspond to similar portions of the object captured by the cameras. A portion of the plurality of images may be presented on a display screen via a graphical user interface. The plurality of images may be grouped based on the correspondence information.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments of the present invention relate generally to systems and processes for transforming a style of video data. In one embodiment, a neural network is used to interpolate native video data received from a camera system on a mobile device in real-time. The interpolation converts the live native video data into a particular style. For example, the style can be associated with a particular artist or a particular theme. The stylized video data can viewed on a display of the mobile device in a manner similar to which native live video data is output to the display. Thus, the stylized video data, which is viewed on the display, is consistent with a current position and orientation of the camera system on the display.

Abstract: Various embodiments of the present invention relate generally to systems and processes for transforming a style of video data. In one embodiment, a neural network is used to interpolate native video data received from a camera system on a mobile device in real-time. The interpolation converts the live native video data into a particular style. For example, the style can be associated with a particular artist or a particular theme. The stylized video data can viewed on a display of the mobile device in a manner similar to which native live video data is output to the display. Thus, the stylized video data, which is viewed on the display, is consistent with a current position and orientation of the camera system on the display.

Abstract: 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 space. 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.

Abstract: 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.

Abstract: 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.

Abstract: Orientation data for image data of an object may be determined. The orientation information may identify camera location and orientation for image data with respect to an object model represented the object at a point in time. A change to the object between different points in time may be identified by identifying a difference in image data associated with different points in time. The change may be presented in a visual representation of the object model in a user interface displayed on a display screen.

Abstract: 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.

Abstract: Reference images of an object may be mapped to an object model to create a reference object model representation. Evaluation images of the object may also be mapped to the object model via the processor to create an evaluation object model representation. Object condition information may be determined by comparing the reference object model representation with the evaluation object model representation. The object condition information may indicate one or more differences between the reference object model representation and the evaluation object model representation. A graphical representation of the object model that includes the object condition information may be displayed on a display screen.

Abstract: 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.

Abstract: 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.

Abstract: Reference images of an object may be mapped to an object model to create a reference object model representation. Evaluation images of the object may also be mapped to the object model via the processor to create an evaluation object model representation. Object condition information may be determined by comparing the reference object model representation with the evaluation object model representation. The object condition information may indicate one or more differences between the reference object model representation and the evaluation object model representation. A graphical representation of the object model that includes the object condition information may be displayed on a display screen.

Abstract: 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.

Abstract: A segmentation of an object depicted in a first visual representation may be determined. The segmentation may include for each image a first respective image portion that includes the object, a second respective image portion that includes a respective ground area located beneath the object, and a third respective image portion that includes a background area located above the second respective portion and behind the object. A second visual representation may be constructed that includes the first respective image portion and a target background image portion that replaces the third respective image portion and that is selected from a target background image based on an area of the third respective image portion relative to the respective image.

Abstract: 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.