An apparatus for optical inspection of sanitaryware includes: a supporting device configured to receive a piece of sanitaryware to be inspected; at least one camera, configured to capture a plurality of images of the piece of sanitaryware under inspection; an automatic arm, movable between a plurality of operating positions relative to the supporting device, wherein the at least one camera is mounted on the automatic arm; a control unit, configured to receive the plurality of images, wherein the control unit is configured to process the plurality of images as a function of reference data to provide diagnostic information regarding the defectiveness of the piece of sanitaryware.

A point cloud data processing apparatus 11 includes a processor configured to acquire first form information that indicates a feature of a form of a first object, specify an object region of a second object that is identified from an image and that corresponds to the first form information, select second-object point cloud data, in point cloud data, that corresponds to the object region, on the basis of the object region, acquire second form information that indicates a feature of a form of the second object, on the basis of the second-object point cloud data, and compare the first form information with the second form information and perform determination as to whether the second object is the first object.

A method for identifying objects by shape in close proximity to other objects of different shapes obtains point cloud information of multiple objects. The objects are arranged in at least two trays and the trays are stacked. A depth image of the objects is obtained according to the point cloud information, and the depth image of the objects is separated and layered to obtain a layer information of all the objects. An object identification system also disclosed. Three-dimensional machine vision is utilized in identifying the objects, improving the accuracy of object identification, and enabling the mechanical arm to accurately grasp the required object.

Provided are methods, performed by an electronic device, for processing an image. The method includes obtaining a first image by photographing a subject. The method further includes obtaining a depth image including information related to a distance from the electronic device to the subject. The method further includes determining whether light reflection exists in the first image. The method further includes obtaining depth information indicating the distance from the electronic device to the subject. The method further includes obtaining a second image by photographing the subject in an activated state of a flash. The method further includes performing pre-processing for matching the first image, the second image, and the depth image. The method further includes obtaining the image from which the light reflection has been removed using at least one of the pre-processed first image, the pre-processed second image, or the pre-processed depth image.

Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.

An electronic device and a method for tracking an object thereof are provided. The electronic device identifies whether there is a first object being tracked, when obtaining an image and rotation information of a camera of the electronic device, corrects state information of the first object using the rotation information, when there is the first object, detects a second object matched to the first object from the image based on the corrected state information, and tracks a position of the second object using an object tracking algorithm.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.

A weld-line generating apparatus includes a point-cloud-data acquiring unit that acquires 3D point cloud data of workpieces to be welded that are arranged in a predetermined space, an edge extracting unit that extracts 3D point cloud data of edges from the 3D point cloud data acquired by the point-cloud-data acquiring unit, a workpiece point-cloud-data generating unit that generates a 3D point cloud data component of each of the workpiece based on 3D point cloud data that is obtained by removing the 3D point cloud data of edges extracted by the edge extracting unit from the 3D point cloud data acquired by the point-cloud-data acquiring unit, and a weld-line generating unit 24 that generates weld lines for the workpieces based on the 3D point cloud data components of the workpieces generated by the workpiece point-cloud-data generating unit.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.

An automated yard audit system is provided. The automated yard audit system includes one or more light detection and ranging (LIDAR) sensors, which are each configured to scan at least a portion of a fulfillment yard, and a controller. The controller is configured to receive scanning data of the yard from each of the LIDAR sensors and generate a virtual representation of the fulfillment yard based on the scanning data. The controller is also configured to identify one or more objects in the fulfillment yard, track movement of the one or more objects in the fulfillment yard, perform an audit of the fulfillment yard, and determine capacity information of the fulfillment yard, based on the virtual representation.