An additive manufacturing apparatus, a computing system, and a method for operating an additive manufacturing apparatus are provided. The method includes obtaining two or more images corresponding to respective build layers at a build plate, wherein each image comprises a plurality of data points comprising a feature and corresponding location at the build plate; removing variation between the features of the plurality of data points; and normalizing each feature to remove location dependence in the plurality of data points.

Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

A spirometry system includes an imaging device configured to capture upper body movement images of a subject during inhalation and exhalation of the subject. The system further includes at least one controller configured to receive the captured images from the imaging device and, based upon the received images, determine at least one of an image-based spirometry flow-volume curve for the subject or an image-based spirometry parameter for the subject.

A method of content production includes generating a survey of a performance area that includes a point cloud representing a first physical object, in a survey graph hierarchy, constraining the point cloud and a taking camera coordinate system as child nodes of an origin of a survey coordinate system, obtaining virtual content including a first virtual object that corresponds to the first physical object, applying a transformation to the origin of the survey coordinate system so that at least a portion of the point cloud that represents the first physical object is substantially aligned with a portion of the virtual content that represents the first virtual object, displaying the first virtual object on one or more displays from a perspective of the taking camera, capturing, using the taking camera, one or more images of the performance area, and generating content based on the one or more images.

One example of an inspection system includes a laser, a magnification changer, and a first camera. The laser projects a line onto a wafer to be inspected. The magnification changer includes a plurality of selectable lenses of different magnification. The first camera images the line projected onto the wafer and outputs three-dimensional line data indicating the height of features of the wafer. Each lens of the magnification changer provides the same nominal focal plane position of the first camera with respect to the wafer.

An image processing device includes a rotation processor and an image processor. The rotation processor receives an input image and generates a temporary image according to the input image. The image processor is coupled to the rotation processor and outputs a processed image according to the temporary image, wherein the image processor has a predetermined image processing width, a width of the input image is larger than the predetermined image processing width, and a width of the temporary image is less than the predetermined image processing width.

A mobile robot is configured to navigate on a sidewalk and deliver a delivery to a predetermined location. The robot has a body and an enclosed space within the body for storing the delivery during transit. At least two cameras are mounted on the robot body and are adapted to take visual images of an operating area. A processing component is adapted to extract straight lines from the visual images taken by the cameras and generate map data based at least partially on the images. A communication component is adapted to send and receive image and/or map data. A mapping system includes at least two such mobile robots, with the communication component of each robot adapted to send and receive image data and/or map data to the other robot. A method involves operating such a mobile robot in an area of interest in which deliveries are to be made.

Described are techniques and systems for secure camera based IMU calibration for stationary systems, including vehicles. Existing vehicle camera systems are employed, with enhanced security to prevent malicious attempts by hackers to try and cause a vehicle to enter IMU calibration mode. IMU calibration occurs when a calibration system determines the vehicle is parked in a controlled environment; calibration targets are positioned at different viewing angles to vehicle cameras to act as sources of optical patterns of encoded data. Features of the patterns are for security as well as for alignment functionality. Images of the calibration targets enable inference of a vehicle coordinate system, from which calculations for IMU mounting error compensations are performed. A relative rotation between the IMU and the vehicle coordinate system are applied to IMU data to compensate for relative rotations between the vehicle and the IMU, thereby improving vehicle slope and bank metrics.

Methods, systems, and apparatus, including medium-encoded computer program products, for pre-processing image data before 3D object tracking includes, in at least one aspect, a method including: receiving, at a first computer, image frames from a camera; identifying, by the first computer, locations of interest in the image frames; finding sequences of the locations, wherein each of the sequences satisfies a motion criterion for locations identified in at least three image frames from the camera; and sending output data for the sequences of the locations to a second computer for processing the sequences in the output data by interpolating between specified 2D positions in specific image frames for the sequences, using timestamps of the specific image frames, to produce a virtual 2D position at a predetermined point in time, which is usable for constructing a 3D track of a ball in motion.

A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation DLN based on the structured 3D model and the object parameters. The pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.