A road surface detection device according to an embodiment includes an image acquisition unit that acquires captured image data output from a stereo camera that captures an imaging area including a road surface on which a vehicle travels, a three-dimensional model generation unit that generates a three-dimensional model of the imaging area including a surface shape of the road surface from a viewpoint of the stereo camera based on the captured image data, and a correction unit that estimates a plane from the three-dimensional model, and corrects the three-dimensional model so as to match an orientation of a normal vector of the plane and a height position of the plane with respect to the stereo camera with a correct value of an orientation of a normal vector of the road surface and a correct value of a height position of the road surface with respect to the stereo camera, respectively.

An edge data network for providing a three-dimensional (3D) character image to a user equipment and an operating method thereof are provided. The edge data network obtains key points information including feature point coordinates related to the body parts of a first user, from a first user equipment via a network, and obtains view points information including virtual position coordinate value information of virtual view points from which a second user views a 3D character image from a second user equipment, measures a key points similarity and a view points similarity by respectively comparing the obtained key points information and view points information with key points information and view points information cached in a data cache, and reads out a 3D character image cached in the data cache based on the measured key points similarity and the measured view points similarity, and transmits the read out 3D character image to the second user equipment.

Methods and systems for developing a sizing system through categorization and selection of prototypes, which can be regarded as the most appropriate fit model, is described. Once categorized and prototypes are selected, recommendations for the sizing of a target body part may be issued.

The present disclosure relates to autonomous driving technology, and provides a method and an apparatus for positioning a movable device, as well as a movable device. The method includes: obtaining point cloud data for a predetermined area above the movable device; extracting, from the point cloud data, a first type of point cloud and a second type of point cloud on a left side and a right side of the movable device, respectively; matching the first type of point cloud and the second type of point cloud to obtain a transform matrix; and determining pose information of the movable device based on the transform matrix. With the above process, the present disclosure can solve the problem in the related art associated with accurate positioning of a movable device when GNSS signals are affected and it is difficult to accurately obtain point cloud data in front of the movable device.

A computer system obtains a digital 3-dimensional model of a body surface, such as a human face. Portions of the surface correspond to reference points in the model. The system obtains image data of a personal care device in use as well as image data of the surface on which the model is based. The system calculates a relative location of the device relative to the reference points based on the image data of the surface. The system determines a true position of the device with respect to the surface based on the image data of the device in use, known physical dimensions of the device, and its relative location, even when the device is partially occluded from view. The system may, based on the true position of the device, cause device to perform actions such as application of a cosmetic or administration of a skin therapy.

Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.

Automated techniques provide for recalibrating cameras in a real space in which puts and takes of items are tracked. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to match one or more features from the selected images with features extracted from the set of calibration images using a trained neural network classifier. The features correspond to points located at displays or structures that remain substantially immobile. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.

Automatic threat detection of volumetric computed tomography (CT) data, including: extracting at least one 3-D object from the volumetric CT data; constructing a feature vector for each of the at least one 3-D objects; and classifying each 3-D object as one of a threat or benign object using the feature vector and a set of truth threat and benign objects.

A method for monitoring the shape of teeth including: producing a three-dimensional digital model of at least one portion of an arch of the patient or “initial reference model”; acquiring at least one two-dimensional image of the arches, referred to as “updated image”, under actual acquisition conditions; analysing each updated image and producing an updated map relating to a piece of discriminating information; optionally determining rough virtual acquisition conditions that approximate the actual acquisition conditions; searching each updated image for a final reference model corresponding to the shape of the teeth, and optionally to the positioning of the teeth, during the acquisition of the updated image; and comparing the shapes of the initial reference model and of the reference model obtained at the end of the preceding steps, referred to as “final reference model”, in order to determine the deformation and/or the movement of teeth between steps a) and b).

A computer-implemented method includes receiving a first 3D model and a second 3D model, wherein the first 3D model represents a first set of object and the second 3D model represents includes a second set of objects. The computer-implemented method further includes scanning each of the first 3D model and the second 3D model to identify the first set of objects and the second set of objects, wherein the first set of objects and the second set of objects have at least one common object. The computer-implemented method further includes comparing the first set of objects to the second set of objects to yield one or more differences. The computer-implemented method further includes sorting each of the one or more differences based on a set of rules to yield a list of differences. A corresponding computer system and computer program product are also disclosed.