Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.

Point cloud data that is missed due to an optical reflection object in measuring point cloud data using a laser scanner is used. A reflection object position calculating device includes a point cloud data receiving unit, a three-dimensional point cloud model generating unit, a missing data part searching unit, a missing data part determining unit, and a reflection object position calculator. The point cloud data receiving unit receives point cloud data. The three-dimensional point cloud model generating unit generates a three-dimensional point cloud model from the received point cloud data. The missing data part searching unit searches for a missing data part of the generated three-dimensional point cloud model. The missing data part determining unit determines whether the found missing data part has a predetermined specific shape. The reflection object position calculator calculates three-dimensional coordinates of the missing data part that is determined as having the specific shape.

An apparatus includes a display screen that includes OLED pixels disposed at a particular pitch in a first plane. A light projector includes light emitting elements disposed in a second plane parallel to the first plane. The light emitting elements are disposed at the same pitch as the OLED pixels or at an integer multiple of the pitch of the plurality of OLED pixels. The light emitting elements are operable to produce light at a wavelength for transmission through the display screen, and the first and second planes are separated from one another by a distance D such that d.sup.2=2*()*(D)/(N), where d is the pitch of the OLED pixels, is the wavelength, and N is a positive integer.

The embodiments disclose a method comprising creating 3D models of an orchard with multiple plants in the form of a densified point cloud using oblique aerial RGB imaging and photogrammetry, identifying and segmenting individual plants of the orchard from the 3D models, simulating sunlight radiation in the 3D models, determining a shading effect of branches and neighboring plants on each individual plant at any time of the day, determining canopy light interception of each plant, analyzing canopy geometry of each plant in the 3D models, forecasting potential yield of each plant based on the measured canopy light interception and calculating nitrogen and water requirements of each plant based on the potential yield and other predetermined field, environmental and climate factors and validating the yield forecasting model using the canopy light interception data by measuring the actual yield for each plant.

A computer-implemented method is provided that includes causing an aerial vehicle to scan an environment in a predesignated pattern, such that a first set of images are captured. The method further includes detecting an emergency scene in the first set of images of the environment. The method further includes determining locations at which the aerial vehicle is to capture a second set of images of the emergency scene in the environment. The method further includes causing the aerial vehicle to acquire the second set of images at the locations. The method further includes determining selected images of the second set of images focused on the emergency scene. The method further includes extracting the selected images from the second set of images, the selected images comprising a representation of the emergency scene.

An electronic device includes: a display panel including a plurality of light-emitting elements and a fingerprint sensing region; a fingerprint sensor disposed below the fingerprint sensing region; and at least one processor electrically connected to the display panel or the fingerprint sensor, and configured to identify an occurrence of an event that causes an operation of receiving a fingerprint through the fingerprint sensor to be executed, obtain a first image of the fingerprint by controlling the display panel such that light-emitting elements disposed in a first light-emitting region within the fingerprint sensing region output light, obtain a second image of the fingerprint by controlling the display panel such that light-emitting elements disposed in a second light-emitting region that does not overlap the first light-emitting region output light, and determine whether the fingerprint is a three-dimensional object, by using the first image and the second image.

A bridge military load classification (MLC) assessment system may increase soldier survivability and/or engineer reconnaissance support to military decision-making process (MDMP) while reducing the required operator level of expertise. A set of at distance 3D dimensioning solutions may include soldier-guided, vector-based measurements, photogrammetry, and/or computer vision (CV) and augmented reality (AR) methodologies-enabling off image plane measurements along all three axes across the image through the image principal point. The solutions may include (1) single image with view of bridge feature that introduces two- or three-point perspective, (2) camera focal length, (3) camera sensor width, (4) laser rangefinder (LRF) vector to the image principal point, and (5) two lines traced along edges of the bridge to identify one vanishing point. A rapid MLC could be calculated from a considerable distance (greater than 100 meters) given an unobstructed below-deck view of the bridge.

Embodiments of this application disclose an object annotation method and apparatus, a movement control method and apparatus, a device, and a storage medium. The method includes: obtaining a reference image recorded by an image sensor from an environment space, the reference image comprising at least one reference object; obtaining target point cloud data obtained by a three-dimensional space sensor by scanning the environment space, the target point cloud data indicating a three-dimensional space region occupied by a target object in the environment space; determining a target reference object corresponding to the target object from the reference image; determining a projection size of the three-dimensional space region corresponding to the target point cloud data and the three-dimensional space region being projected onto the reference image; and performing three-dimensional annotation on the target reference object in the reference image according to the determined projection size.

A method for generating a 3D combined model that represents a scene includes acquiring the coordinates in an external geodetic frame, acquiring at least one 3D primary model representing the scene, which model includes primary model elements, oriented in the geodetic frame. The primary model elements are assigned measured primary physical property values in the form of coordinates in a local three-dimensional coordinate system of the corresponding point. The method further includes generating a 3D secondary model representing the scene, wherein at least some secondary model elements are assigned coordinates in the local coordinate system and at least one secondary physical property value. At least some secondary model elements are assigned coordinates in the external geodetic frame. The primary and at least one secondary model are then combined. Also disclosed are a related system and reference members for use in the system.

A technique for avoiding obstacles by an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area below the UAV; analyzing the aerial image to identify a shadow in the aerial image cast by an object rising from the ground area; determining a pixel length of the shadow in the aerial image; calculating an estimated height of the object based at least on the pixel length of the shadow and an angle of the sun when the aerial image is acquired; and generating a clearance zone around the object having at least one dimension determined based on the estimated height, wherein the clearance zone represents a region in space to avoid when navigating the UAV.