A positioning device combining mark point positioning and intelligent reverse positioning and method thereof, comprising a binocular camera, a third camera, and a laser; the laser is used for emitting laser projection, the binocular camera is used for acquiring images with laser lines and reflective mark points on the surface of the scanned object, and the third camera is used for acquiring images with coding points and mark points in the peripheral environment; the method comprises the following steps of: S1. calibrating parameters of each camera under different scanning modes, and enabling the parameters of each camera to synchronously and correspondingly transform when the scanning modes are switched; S2. judging and switching the scanning mode into a mark point mode or an intelligent reverse tracking mode through the scanning scene. The two positioning modes are flexibly switched, and the use of a user is facilitated.

A stereo camera calibration method includes: controlling a stereo camera assembly to capture a sequence of stereo image pairs; simultaneously with each capture in the sequence, activating a rangefinder; responsive to each capture in the sequence, updating calibration data for point cloud generation by: detecting matching features in the stereo image pair, and updating a first portion of the calibration data based on the matched features; updating an alignment of the rangefinder relative to the stereo camera assembly, based on the updated first portion of the calibration data, and a detected position of a beam of the rangefinder in a first image of the stereo image pair; and updating a second portion of the calibration data based on the detected position of the beam of the rangefinder in the first image of the stereo image pair, the updated rangefinder alignment, and a depth measurement captured by the rangefinder.

An apparatus includes an interface and a processor. The interface may be configured to receive pixel data representing respective fields of view of two or more cameras arranged to obtain a predetermined field of view, where the respective fields of view of each adjacent pair of the two or more cameras overlap. The processor may be configured to process the pixel data arranged as video frames and perform a fixed pattern calibration for facilitating multi-view stitching. The fixed pattern calibration may comprise applying a pose calibration process to the video frames. The pose calibration process generally uses (i) intrinsic parameters, a respective translate vector, a respective rotation matrix, and distortion parameters for each lens of the two or more cameras and (ii) a calibration board to obtain configuration parameters for the respective fields of view of the two or more cameras. The pose calibration process may comprise changing a z value of the respective translate vector for each lens of the two or more cameras to at least one of a middle distance value and a long distance value while maintaining the respective rotation matrix for each lens of the two or more cameras unchanged.

An improved method, system, and apparatus is provided to perform camera calibration, where cameras are mounted onto a moving conveyance apparatus to capture images of a multi-planar calibration target. The calibration process is optimized by reducing the number of images captured while simultaneously preserving overall information density.

Systems and methods for macro stereo time-lapse photography, producing a stereographic time-lapse digital video, and macro stereographic time-lapse digital videos. A method of producing a sequence of time-lapse stereographic images of a subject, by positioning a camera with a macro lens at a first position relative to the subject; using the camera to obtain a first stack of images of the subject from the first position; positioning the camera at a second position relative to the subject; using the camera to obtain a second stack of images of the subject from the second position; and storing the first stack of images and the second stack of images as a stack pair; and then selectively repeating.

A full-automatic calibration method and apparatus oriented to a structured light 3D vision system are disclosed. For an erected 3D vision system, full-automatic calibration can be completed without moving the 3D vision system; and for an unfixed 3D vision system, full-automatic calibration can be completed without manual operation. By using the full-automatic calibration method oriented to the structured light 3D vision system, on one hand, non-professionals can easily complete calibration of structured light 3D imaging; and on the other hand, a problem of calibrating a large number of 3D cameras can also be solved.

Some embodiments are directed to an image director of a patient monitoring system to obtain calibration images of a calibration sheet or other calibration object at various orientations and locations. The images are then stored and processed to calculate camera parameters defining the location and orientation of the image detector and identifying internal characteristics of the image detector, and the information are stored. The patient monitoring system can be re-calibrated by using the image detector to obtain an additional image of a calibration sheet or calibration object. The additional image and the stored camera parameters are then used to detect any apparent change in the internal characteristics of the image detector (10)(S6-4).

Three-dimensional image calibration and presentation for eyewear including a pair of image capture devices is described. Calibration and presentation includes obtaining a calibration offset to accommodate flexure in the support structure for the eyewear, adjusting a three-dimensional rendering offset by the obtained calibration offset, and presenting the stereoscopic images using the three-dimension rendering offset.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

The present invention discloses a multispectral stereo camera self-calibration algorithm based on track feature registration, and belongs to the field of image processing and computer vision. Optimal matching points are obtained by extracting and matching motion tracks of objects, and external parameters are corrected accordingly. Compared with an ordinary method, the present invention uses the tracks of moving objects as the features required for self-calibration. The advantage of using the tracks is good cross-modal robustness. In addition, direct matching of the tracks also saves the steps of extraction and matching the feature points, thereby achieving the advantages of simple operation and accurate results.