18 results about "Camera matrix" patented technology

The invention belongs to the technical field of augmented reality (XR) and computer vision crossing, relates to fusion application in immersive digital performance, and provides a virtual actor reconstruction and immersive presentation system based on 4D Gaussian splash modeling and XR space positioning. The system comprises a set of spherical multi-camera-position high-synchronization camera shooting matrix used for capturing dynamic images of actors; performing dynamic modeling on the multi-angle image through a 4D Gaussian splashing technology, and outputting a virtual actor point cloud model which can be used by XR equipment; vPS visual positioning and an SLAM tracking module are combined, and precise mapping positioning of a performance space is achieved in AR / MR equipment. The method supports the immersive watching of the actor image at the audience end at a 360-degree free visual angle, and realizes the natural presentation of the virtual actor without dead angles and wearing in cooperation with shielding judgment and a real-time rendering engine. The system is widely applicable to on-site entertainment scenes such as immersive theaters, text travel performances, brand activities, concerts and television programs.

The utility model discloses a multi-camera image acquisition device for building a three-dimensional model of a bridge structure prefabricated part, which comprises a portal steel frame and a track assembly, and the portal steel frame comprises truss columns respectively positioned on two sides of the bridge structure prefabricated part and a truss beam positioned above the bridge structure prefabricated part. The truss girder is mounted at the tops of the two truss columns; cameras are arranged on the truss columns and the truss girders at intervals to form a camera matrix which is fixed in relative position and is used for acquiring image data of the bridge structure prefabricated parts; the rail assembly comprises sliding rails arranged corresponding to the two truss columns respectively, the sliding rails are parallel to the length direction of the bridge structure prefabricated part, and the truss columns are installed on the corresponding sliding rails in a sliding fit mode. According to the multi-camera image acquisition device for building the three-dimensional model of the prefabricated part of the bridge structure, image data acquired by the cameras can be fused by fixing the position relation between the cameras, so that the three-dimensional model of the prefabricated part of the bridge structure is built.

This application provides a method, electronic device, and storage medium for object implantation in multi-degree-of-freedom (DOF) video. The method includes: obtaining the coordinates of a real camera matrix in a real-world coordinate system, wherein the real camera matrix is ​​used to capture multi-DOF video; constructing a virtual coordinate system based on the coordinates of the real camera matrix in the real-world coordinate system, wherein the virtual coordinate system is the coordinate system corresponding to the virtual camera matrix in virtual space; mapping the trajectory information of the real camera matrix in the real-world coordinate system to the trajectory information of the virtual camera matrix in the virtual coordinate system; and associating the trajectory information of the virtual camera matrix in the virtual coordinate system with a virtual object to be implanted in the multi-DOF video to obtain implantation information for implanting the multi-DOF video. This application provides a foundation for accurate matching between virtual objects and multi-DOF video.

The invention provides an unmanned aerial vehicle automatic inspection route generation method and system based on visual identification, and relates to the technical field of unmanned aerial vehicle inspection, and the method comprises the steps: collecting an inspection video stream of an inspection region through a camera device carried by an inspection unmanned aerial vehicle; extracting an inspection image frame and carrying out target detection on the inspection image frame based on a deep learning model to generate a multi-scene inspection target point; based on a camera matrix and a digital elevation model, pixel coordinates of the multi-scene inspection target point are converted into actual geographic coordinates; automatically planning a structured inspection route of the inspection unmanned aerial vehicle according to the actual geographic coordinates in combination with a path curvature constraint condition; a structured inspection route is converted into a recognizable route instruction file and is imported into a flight control system of the inspection unmanned aerial vehicle, so that high efficiency, precision, automation and intelligence of the whole inspection process of the unmanned aerial vehicle are realized by fusing visual recognition and intelligent route planning technologies, and the system adaptability, the inspection quality and the operation safety are greatly improved.

The application discloses a kind of intelligent detection building health and risk prediction teaching system, belong to intelligent construction teaching field, including: through intelligent car and the high-definition camera matrix of unmanned aerial vehicle terminal carrying is collected building data and is transmitted to operation super brain;Through the AI recognition big model and structure safety, natural disaster, personnel safety three risk assessment sub-models built-in in operation super brain, the multi-level comprehensive risk assessment of building state is carried out;Through the hazard identified in building digital twin 3D model corresponding position visualization presentation and generating graded warning signal by wisdom management platform, platform supports student login operation to carry out picture digitization, autonomous operation practice and complete experiment report;After completing on-campus laboratory simulation and course practice, form course outline and experiment report and popularize to other colleges and universities.The application enables students to visually observe building hazards, complete risk prediction operations, and promote cross-campus sharing of teaching resources.

A method for creating an image of a base of a container uses a matrix camera. The matrix camera captures a series of individual captures of regions of the base of the container which overlap in sections, while the base of the container is transilluminated by a light source arranged on the side opposite the matrix camera. A digital image of the base of the container is compiled from the individual captures. The invention is characterized in that the region of the base of the container captured in an individual capture is defined by an illumination structure, that the illumination structure is shifted relative to the base of the container between two individual captures, and that the container and the matrix camera remain rotationally invariant to one another during the capturing of the series of individual captures. The invention also relates to a device for carrying out the method.

The invention discloses an intelligent cockpit gesture recognition method fusing attention and map updating, and the method comprises the steps: building a space model of a cockpit, presetting a local attention interval for each seat of the cockpit, associating the local attention interval with a multi-camera matrix, and constructing a seat-interval-camera mapping relation; the method comprises the following steps: synchronously acquiring image data in a cabin through a multi-camera matrix, operating a hand detection model to detect a hand image, and preliminarily classifying the hand image into a local attention interval; performing gesture recognition on the hand image, and outputting a gesture recognition result; building a cockpit knowledge graph, and dynamically updating a node and edge relationship of the cockpit knowledge graph according to the passenger state change and / or the gesture recognition result; and performing authority verification and conflict arbitration based on a gesture recognition result and the cabin knowledge graph, sending a control instruction passing arbitration to a corresponding cabin actuator, and performing closed-loop optimization based on user feedback. According to the invention, the accuracy, real-time performance and system robustness of gesture recognition can be improved.

The application discloses a kind of methods for detecting and repairing non-ferrous metal strip surface crack synchronously online, comprising the following steps: system initialization: after system power on, a series of self-checking and calibration operations are performed to ensure that all components are in standby state and initial position; online detection: drive equipment drives the rotation of the reel, and the industrial camera matrix arranged on the lower side of the industrial camera mounting bracket detects the surface crack of the strip passing below; when the online detection result is "crack found", the synchronous mechanism is connected to the main shaft of the drive equipment through the reversing device. Through the multi-level automatic process of detection-repair-reinspection, the traditional mode of relying on manual slow inspection and manual operation during shutdown is replaced. The strip can complete defect identification and processing during high-speed operation without the need to stop or slow down, greatly improving production efficiency while reducing labor intensity, eye strength and skill requirements of workers.

The application discloses a single-image three-dimensional head reconstruction method and system based on a guided diffusion model, and the method comprises the following steps: obtaining a head portrait from a single image to be reconstructed; reconstructing a three-dimensional head model according to the head portrait; weakly projecting and mapping the three-dimensional head model to the head portrait under a predicted camera matrix, and obtaining a three-dimensional head model with front texture by using pixel information of the head portrait; performing cylindrical UV unfolding on the three-dimensional head model with the front texture to obtain a two-dimensional UV texture map and record a UV mapping relationship; inputting the two-dimensional UV texture map into a guided diffusion network model to predict the texture of an invisible area and obtain a complete two-dimensional UV texture map; and pasting the complete two-dimensional UV texture map back to the three-dimensional head model. The application can reconstruct a high-fidelity, artifact-free and realistic three-dimensional head model with rich texture details from a single portrait of an individual of different ages, genders, races and facial expressions.

1. A computer-implemented method for processing anatomical imaging data, the method comprising: acquiring, for a plurality of anatomical specimens, 3D imaging data of a particular body part of the anatomical specimens among the plurality of anatomical specimens; acquiring 2D images of the particular body part of the anatomical specimens from a plurality of different viewpoints relative to the particular body part; determining a plurality of camera matrices P corresponding to the acquired 2D images; generating a composite 2D image by projecting the acquired 3D imaging data according to the plurality of camera matrices P; and associating the acquired 2D image with one of the composite 2D images as a training data pair according to the plurality of camera matrices P and the plurality of viewpoints. The method further comprises generating a training data set for use in training an artificial intelligence algorithm using the training data pairs.

The invention provides an anti-upheaval method and device for a combined visual system or an enhanced flight visual system, and the method comprises the following steps: determining the position of a real horizon line in a current EVS image, and obtaining the position of the real horizon line on the EVS image through a camera matrix of the EVS, the latitude and longitude of an airplane, the height of the airplane and an airport plane equation. And determining the maximum elevation line position in the current EVS image. Finding an image point with the highest position in the EVS image for elevation points in a given range, and obtaining a straight line which passes through the point and is parallel to the horizon, namely the maximum elevation line. And starting or inhibiting the anti-upheaval function according to the input of the pilot.

The present application relates to the field of image super-resolution, more particularly, to an image reconstruction method, in particular to a Fourier stack image reconstruction method based on a deep learning network. By using a reflective far-field Fourier stack imaging system, a series of low-resolution images are captured by a camera array, deep learning is combined with traditional Fourier stack, and a neural network is used to reconstruct low-resolution images, reduce the number of low-resolution image acquisition, reduce the redundancy requirement of traditional Fourier stack, and improve the sampling speed and reconstruction speed of Fourier stack imaging.

A system for producing a three dimensional model incudes functionalities of: presenting to a user a reference image of an item and facilitating uploading of a three dimensional model created by the user of the same item. The system then also being designed for obtaining parameters of a unique camera matrix in which a rendering of the three dimensional model appears substantially similar the reference image.

The invention discloses a robot eye movement control method and system based on speed control, and the method comprises the steps: obtaining the eye movement data of an operator, and processing the eye movement data; the eye movement data is converted into a speed control instruction of the mechanical arm based on the speed eye movement control normal form; multi-view-angle environment information is obtained through an omni-directional camera matrix arranged at the working end of the mechanical arm, a multi-view-angle view is rendered on a user interface, and view angles are automatically switched according to the falling point of a fixation point; and the speed control instruction is received, kinematics transformation and environment state estimation are carried out, joint motion information of the mechanical arm is obtained, and the mechanical arm is controlled to execute a task. On the basis that direct position mapping is carried out on tail end movement of the robot through eye movement data, the movement speed of the robot serves as an interaction object, speed mapping of the robot is carried out through a gazing sight falling point distance control center displacement vector in the interaction process, and the performance is good in the aspects of continuous movement and dynamic adjustment of the robot.

This disclosure relates to a method and system for simultaneously tracking the six-degree-of-freedom orientations of a movable object and a movable camera. The method includes the following steps: capturing a series of images with a movable camera; extracting several environmental feature points from these images; matching these environmental feature points to calculate several camera matrices of the movable camera; and then calculating the six-degree-of-freedom orientation of the movable camera using these camera matrices. Simultaneously, several feature points of the movable object are inferred from these images captured by the movable camera; using the camera matrices corresponding to each image, as well as predefined geometric and time constraints, the coordinates of these feature points of the movable object are corrected; and then the six-degree-of-freedom orientation of the movable object is calculated using these corrected feature point coordinates and their corresponding camera matrices.

The invention discloses a method for online detecting and synchronously repairing surface cracks of a non-ferrous metal strip, which comprises the following steps of: initializing a system: after the system is electrified, executing a series of self-inspection and calibration operations to ensure that all components are in a standby state and an initial position; and on-line detection: driving equipment drives a winding drum to rotate to roll the strip, an industrial camera matrix arranged on the lower side of an industrial camera mounting frame detects cracks on the surface of the strip passing through the lower side, and when an on-line detection result is that the cracks are found, a reversing device drives a synchronizing mechanism to be connected with a main shaft of the driving equipment. Through a multi-stage automatic process of detection, repair and reinspection, a traditional mode of depending on manual low-speed inspection and shutdown manual operation is replaced. Defects of the strip can be recognized and processed in the high-speed running process, shutdown or speed reduction is not needed, the production efficiency is greatly improved, and meanwhile the labor intensity, eyesight and proficiency requirements of workers are lowered.

The invention provides a data processing method and system for an AI practical training computing power workstation, and relates to the technical field of data processing, a four-camera matrix sensor group is built, practical training four-camera data acquisition is performed on practical training area information, practical training four-camera acquisition data is obtained, function analysis optimization and data feature processing are performed according to the practical training four-camera acquisition data, and the AI practical training computing power workstation is obtained. Obtaining practical training feature processing data; performing parallel mapping computing power processing state analysis on the practical training four-camera acquisition data and the practical training feature processing data to obtain computing power processing state judgment information; computing power resource allocation analysis is carried out according to the computing power processing state judgment information, computing power resource allocation is carried out according to the computing power resource allocation analysis information, and computing power resource allocation data is obtained. Multi-dimensional high-quality data collection, safety grading processing and dynamic precise computing power allocation can be achieved.