A system and method for using images from a commodity camera for object scanning, reverse engineering, metrology, assembly, and analysis are disclosed. A particular embodiment includes a mobile imaging system to: enable a user to align an object to be analyzed on a turntable with a stencil; issue commands, by use of a data processor, to the turntable for automatic rotation of the turntable and the object thereon to a particular orientation for a camera of a mobile imaging device; capture a plurality of images of the object being analyzed at different automatic rotations of the turntable; upload the plurality of images of the object to a server via a network interface and a data network; and cause the server to generate a three dimensional (3D) model of the object from the plurality of images of the object.

Techniques for automatic image tagging and selection at a mobile device include generating a smart image tagging model by first training an initial model based on different angles of image capture of subject vehicles, and then re-training the trained model using weights discovered from the first training and images that have been labeled with additional tags indicative of different vehicle portions and/or vehicle parameters. Nodes that are training-specific are removed from the re-trained model, and the lightweight model is serialized to generate the smart image tagging model. The generated model may autonomously execute at an imaging device to predict respective tags associated with a stream of frames; select, capture and store respective suitable frames as representative images corresponding to the predicted tags; and provide the set of representative images and associated tags for use in determining vehicle damage, insurance claims, and the like.

Various embodiments include a computer-implemented method for augmenting a physical infrastructure with an information element. The method may include: acquiring data of a spatial environment infrastructure, dimensioned by a coordinate system; defining a second spatial environment as a subspace of the first and dimensioned by its own coordinate system with a first marker item and a second marker item; creating a SLAM map of the second environment with two SLAM marker items; recognizing the SLAM identifiers; aligning the coordinate system of the SLAM map to the coordinate system of the second environment; recognizing a first and a second marker identifier; determining an absolute position and orientation of the second spatial environment relative to the first environment; defining the information element in relation to a component of the infrastructure; determining coordinates of the element in the second environment; and augmenting the physical infrastructure with the information element at the spatial coordinates.

A collation device includes a processor configured to, by executing a program: (a) acquire a photographed image including a collation area provided on an object and a reference position area that serves as a reference for a position of the collation area; (b) detect the reference position area from the photographed image; (c) set a peripheral image area in the photographed image based on the reference position area and predetermined relative coordinates; and (d) detect the collation area in the peripheral image area.

A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

A first 2D recording of a specified reference view of an object is captured by a camera and, starting from the first 2D recording, a user's starting location relative to the object is ascertained by a computer vision module. Starting from the origin of a coordinate system as the starting location of the camera, one or more specified and/or settable relative positions in the vicinity of the object and/or in the object are determined as one or more locations for the respective perspective of the camera for taking at least one second 2D recording. The respective location in an object view on a display of the camera is displayed by a respective first augmented reality marker on the ground and/or on the object. The alignment of the camera with regard to angle and rotation with the perspective corresponding to the respective location is performed in this case by second augmented reality markers as auxiliary elements.

A monocular visual simultaneous localization and mapping (SLAM) data processing method. The SLAM data processing method comprises: obtaining rotation angular velocities and accelerations of a camera cyclically; obtaining a plurality of feature point pairs in two frames of images acquired by the camera, and obtaining pixel coordinate values of feature points in the feature point pairs, where each of the feature point pairs includes two feature points that correspond to a same feature of a same object and that are respectively in the two frames of images; obtaining to-be-selected rotation matrices and to-be-selected displacement matrices according to the pixel coordinate values; obtaining a reference rotation matrix of the camera according to the rotation angular velocities, and obtaining a reference displacement matrix of the camera according to the accelerations; and filtering the to-be-selected rotation matrices and the to-be-selected displacement matrices according to the reference rotation matrix and the reference displacement matrix.

An autonomous marking apparatus comprising a propulsion system, a location sensor, a payload assembly, one or more marking sensors, a transceiver, a data store, and a processor. The location sensor is arranged to determine the location of the apparatus. The payload assembly is arranged to carry a payload of marking material. The one or more marking sensors are arranged to scan an area in proximity to the apparatus. The transceiver is arranged to exchange data with a remote server via a data network. The data store is arranged to store a portion of the data. The processor is arranged to receive data from the location sensor, the one or more marking sensors, and from the transceiver. The processor is also arranged to send data to the transceiver and control the delivery of the payload at the location of the apparatus.

Provided are a 3D reconstruction method and apparatus, an electronic device and a storage medium. The 3D reconstruction method comprises: using a plurality of cameras with different viewing angles to image a symbol to obtain a symbol image, a reference object for camera calibration being called the symbol, the symbol including a plurality of markers, and each of the markers having a corresponding ID number; identifying the ID number of the marker in the symbol image and searching for world coordinates corresponding to the marker according to the ID number; computing an external parameter matrix of the camera according to marker coordinates of a camera coordinate system and marker coordinates of a world coordinate system, and unifying point clouds under the world coordinate system to obtain a plurality of point clouds under different viewing angles; and stitching the plurality of point clouds together to obtain a 3D reconstructed image.

The present application discloses a coding pattern, coding and reading methods for the same, a calibration board, and a calibration method. In the present application, the coding pattern comprises four positioning blocks, wherein three of the positioning blocks are located at three corner portions of the coding pattern, and the remaining positioning block only contacts one edge of the coding pattern, thereby forming an asymmetrical distribution configuration of the four positioning blocks. The invention can replace a two-dimensional code standard in the related art, saving the licensing and manufacturing costs of using two-dimensional code generation software in the related art, and is not subject to usage restrictions of the two-dimensional code generation software in the related art. The invention enables accurate and fast positioning of the coding pattern, facilitates quick discovery of an initial coding block position in a coding region, and allows a customizable size of the coding pattern and a customizable size of a coding region in the coding pattern according to a data volume of an application scenario, thereby providing flexibility in configuring data information recorded in the coding pattern. In addition, coding of the coding region is simple, thereby improving efficiency.