Provided are methods for deep learning-based camera calibration, which can include receiving first and second images captured by a camera, processing the first image using a first neural network to determine a depth of the first image, processing the first image and the second image using a second neural network to determine a transformation between a pose of the camera for the first image and a pose of the camera for the second image, generating a projection image based on the depth of the first image, the transformation of the pose of the camera, and intrinsic parameters of the camera, comparing the second image and the projection image to determine a reprojection error, and adjusting at least one of the intrinsic parameters of the camera based on the reprojection error. Systems and computer program products are also provided.

Techniques for augmenting a reality captured by an image capture device are disclosed. In one example, a system includes an image capture device that generates a two-dimensional frame at a local pose. The system further includes a computation engine executing on one or more processors that queries, based on an estimated pose prior, a reference database of three-dimensional mapping information to obtain an estimated view of the three-dimensional mapping information at the estimated pose prior. The computation engine processes the estimated view at the estimated pose prior to generate semantically segmented sub-views of the estimated view. The computation engine correlates, based on at least one of the semantically segmented sub-views of the estimated view, the estimated view to the two-dimensional frame. Based on the correlation, the computation engine generates and outputs data for augmenting a reality represented in at least one frame captured by the image capture device.

A client computing system (CCS) receives a download including (i) an image representative of a vehicle component, (ii) symbol data associated with a first symbol, (iii) a set of one or more selectable identifiers, and (iv) supplemental information associated with the vehicle component. Each selectable identifier can indicate a respective portion of the supplemental information. After receiving the download, the CCS displays the image and the first symbol without displaying the set and the supplemental information. While the image and the first symbol are displayed without the set, the CCS receives a first input corresponding to selection of the first symbol. The CCS then responsively displays the set. While the set is displayed, the CCS receives a second input corresponding to selection of a first selectable identifier from the set. The CCS then responsively displays the respective portion of the supplemental information indicated by the first selectable identifier.

A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.

Disclosed is a radio-visual approach for device localization. In particular, a mobile device may receive, via radio signal(s) emitted by transmitter(s) in a vehicle that is substantially proximate to the mobile device, radio data that represents vehicle information associated with the vehicle, and may then determine or obtain a location of the vehicle in accordance with that vehicle information. Also, the mobile device may capture an image of the vehicle and may use that image as basis for determining a relative location indicating where the mobile device is located relative to the vehicle. Based on the location of the vehicle and on the relative location of the mobile device, the mobile device may then determine a location of the mobile device.

This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying out an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collects any one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to carry out an excavation routine. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, and helping prepare a digital terrain model of the site as part of a process for creating the excavation routine.

A method includes obtaining a plurality of images that are continuously captured, detecting a first region indicating a feature value corresponding to a license plate, in a region that indicates a feature value corresponding to a vehicle and that is included in a first image among the plurality of images, based on a feature value of each of the plurality of images, determining a second region in the plurality of images, the second region indicating a feature value corresponding to a license plate, at least a part of the second region overlapping the first region in an image different from the first image, and outputting the second region as a license plate region.

The present disclosure provides an image processing device, image processing method, and image processing system that are capable of freely deciding a position of a cutout region when cutting out an image from an original image. The image processing device includes an object detection unit configured to detect an object in a first image, and a cutout region deciding unit configured to decide, as a cutout region, a region positioned in a relative direction based on a position at which the object is detected in the first image, the relative direction varying depending on a detection condition.

An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

A calibration apparatus and method suitable for calibration of an offset sensor of a subject vehicle. The calibration apparatus comprises a reference structure that is placed into a reference locus using image data generated by a camera associated with the reference structure. An offset-target structure is then placed into position by coupling the offset-target structure to the reference structure, the coupling providing an appropriate locus for the offset-target structure during calibration of the offset sensor. The coupling restricts the linear and rotational displacement of the offset-target structure during calibration.