549 results about "Rotation matrix" patented technology

The invention belongs to the technical field of numerical control machine tool machining, and particularly discloses a numerical control machine tool machining reference point correction method and system, a terminal and a medium. Based on the three-dimensional coordinate information, the actual position of a preset reference point on the machine tool is extracted and compared with a preset theoretical reference point, and a deviation value is calculated; executing coordinate deviation aggregation analysis according to the deviation value, and determining coordinate system correction parameters including a translation vector and a rotation matrix; the corrected parameters are issued to a numerical control machine tool control system, and a current workpiece coordinate system is updated; and after the coordinate system is updated, the machining task continues to be executed, the machining state is monitored in real time, and if it is detected that the deviation exceeds a threshold value, the measurement and correction process is automatically triggered again. Automatic correction of the reference point and real-time compensation of the workpiece coordinate system can be achieved, the machining precision is improved, manual intervention is reduced, and the method is suitable for high-precision numerical control machining scenes.

The invention discloses a point cloud registration method, system and device and a medium, and relates to the technical field of three-dimensional reconstruction of scenes, and the method comprises the steps: obtaining a source point cloud and a target point cloud; semantic segmentation is carried out according to spatial structure characteristics of the point clouds, and plane points and non-plane points of the source point clouds and the target point clouds are obtained respectively; two-way distance search constraints are applied to planar points and non-planar points in the source point cloud and the target point cloud respectively, and a corresponding relation of two-way matching constraints is established; point-to-surface registration and point-to-point registration based on the maximum correlation entropy criterion are carried out on the plane points and the non-plane points after bidirectional constraint, and rotation matrixes and translation vectors of the plane points and the non-plane points are respectively obtained through multiple iterations; weighting the rotation matrix and the translation vector according to an adaptive weight, and applying the weighted rotation matrix and translation vector to the source point cloud to obtain the source point cloud after pose transformation; according to the method, the nearest neighbor corresponding relation from the source point cloud to the target point cloud and from the target point cloud to the source point cloud is considered, and the matching accuracy of the corresponding points is improved.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

The invention discloses an unmanned ship displacement attitude high-precision fusion method based on adaptive Kalman filtering, and belongs to the technical field of ship sensing positioning, and the method comprises the steps: building a discrete time state space model of a navigation data state through employing a discrete integration mode based on the navigation data state; obtaining a system state equation by means of a rotation matrix and a median theorem; obtaining an observation transfer matrix through coordinate system information, a geodesic line length between two points and an azimuth angle, and obtaining a state observation equation by combining noise data; based on the system state equation and the state observation equation, using an adaptive Kalman filtering algorithm to obtain displacement attitude information of the unmanned ship; detecting the prediction data of the unmanned ship by using a sliding window method; and when the predicted data is detected to be abnormal, substituting the predicted data into Kalman filtering for correction. According to the invention, through state modeling, fusion algorithm, anomaly detection and anomaly correction, the precision, reliability and robustness of obtaining the displacement attitude information of the unmanned ship are improved.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention relates to a course axis drift suppression method and device, a holder and a medium, and the method comprises the steps: receiving original data of a current course, the original data comprising an absolute angle outputted by a course encoder, and an acceleration and an angular velocity outputted by an IMU module; the original data are converted into a unified coordinate system through a rotation matrix, and mechanical installation deviation is compensated; performing data fusion on the compensated original data through a preset fusion algorithm; and outputting the fused absolute angle to a servo mechanism of the holder, and by adopting the method, through real-time sensor data alignment, a dynamic compensation algorithm and a fusion algorithm, a course drift suppression mechanism which is resistant to dynamic interference and low in cost is constructed, and static drift lt can be realized; the dynamic tracking error is 1t, 0.01 degree / h and 1t; and the technical index is 0.05 degree.

The invention belongs to the technical field of computer vision, deep learning and time-space prediction, and discloses an end-to-end time-space prediction method based on improved three-dimensional rotation position coding, which is suitable for various time-space sequence prediction scenes such as weather, traffic flow and the like. According to the invention, through four key improvements, a position coding mechanism is optimized; three-dimensional coding proportions of time, height and width are dynamically adjusted so as to adapt to different scenes; fusing the absolute time and the relative space position, and strengthening local space-time correlation modeling; the position information directly guides attention calculation, and the fusion with an Attention module is deepened; and a rotation matrix cache mechanism is introduced to reduce redundant calculation. Meanwhile, the model is matched with a Patch embedding layer, an adaptive Transform encoder and an MLP de-wharf, a complete link of'feature embedding-position encoding-space-time fusion-prediction output 'is constructed, and the precision, generalization and reasoning efficiency of space-time prediction are effectively improved.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention belongs to the technical field of space remote sensing surveying and mapping, and particularly relates to a monitoring system and method for a front and rear view visual axis included angle and absolute direction of a surveying and mapping camera. The method comprises the following steps: S1, through side sway of a satellite platform, taking a time for sky imaging calibration as a reference time; s2, calculating a rotation matrix from a front and rear view ground camera measurement coordinate system to a star camera coordinate system at a reference moment; s3, calculating respective thermal deformation matrixes of the star camera coordinate system, the front-view camera measurement coordinate system and the rear-view camera measurement coordinate system; s4, obtaining a real-time rotation matrix from the measurement coordinate system of the front-view camera to the coordinate system of the star camera and a real-time rotation matrix from the measurement coordinate system of the rear-view camera to the coordinate system of the star camera at the measurement moment; and S5, carrying out arc cosine calculation on the two absolute pointing vectors to obtain a visual axis included angle between the front-view camera and the rear-view camera at the measurement moment. According to the invention, while the geometric accuracy of the surveying and mapping camera is ensured, the real-time performance of measurement and the comprehensiveness of subsystem monitoring are realized at the same time.

The invention discloses a remote sensing image cloud removal method and system based on geographic coordinate embedding, and the system employs a geographic coordinate system embedding module to encode pixel longitude and latitude information into high-dimensional features, and achieves the joint modeling of an absolute position and a relative space relation through spherical coordinate mapping and a rotation matrix. And in combination with a multi-head self-attention mechanism and residual connection, the position sensitivity of the feature space is enhanced. Meanwhile, by using a multi-level feature extraction network and a cloud distortion compensation module, through a residual block and convolution operation, cloud interference features are extracted, a position sensing correction vector is generated, nonlinear offset of a cloud coverage area is corrected, and earth surface textures are recovered. According to the method, the adaptability of a cross-regional scene is remarkably improved, texture distortion and information loss of a large-scale cloud region are effectively reduced, the method has the advantages of high precision and high robustness in the fields of remote sensing image preprocessing, agricultural monitoring, land cover change analysis and the like, and a new scheme is provided for high-quality application of optical remote sensing data.

The invention discloses a three-dimensional point cloud registration method and system based on multi-feature dynamic weighting. The method comprises the following steps: firstly, acquiring a source point cloud and a target point cloud to be registered and performing down-sampling; secondly, extracting geometric features of the source point cloud and the target point cloud, including curvatures and normal angles of all points; extracting a target point from the target point cloud, performing point pair matching by using a multi-constraint weighted scoring mechanism and a dynamic weight optimization strategy, and taking a point pair with a minimum dynamic weighted difference function value as a matching point pair; screening the matching point pairs according to the normal angle difference threshold value and the curvature difference threshold value, if the normal angle difference score is smaller than or equal to the normal angle difference threshold value and the curvature difference score is smaller than or equal to the curvature difference threshold value, reserving the matching point pairs, and otherwise, rejecting the matching point pairs; and finally, solving a rotation matrix and a translation vector according to the reserved matching point pairs to realize point cloud registration. The problems that a traditional ICP algorithm is sensitive to the initial attitude of the point cloud, the anti-noise capability is weak, and feature utilization is insufficient are solved.

The invention discloses a bridge tower section attitude intelligent prediction method and system based on multi-point cooperative localization, and the method comprises the steps: S100, selecting a plurality of observation points on two adjacent side surfaces of an installed section, obtaining the space coordinates of the observation points through a laser reflector, and carrying out the cooperative measurement through a plurality of total stations; s200, performing attitude change prediction on the to-be-installed segment based on the rigid body transformation mathematical model, calculating the optimal attitude of the to-be-installed segment by using a rotation matrix and a translation vector, optimizing attitude estimation by excessive-point redundant information, and controlling an accumulative error; s300, performing BIM modeling on the bridge tower structure, performing high-precision three-dimensional rendering, converting complex monitoring data into a visual graphical interface, and displaying a segment state in real time; s400, according to data comparison, analysis is carried out in combination with errors, and if the errors with design data are large, corresponding adjustment is carried out; and S500, the steps are repeated till all the bridge tower sections are accurately positioned and installed. And the erection precision and the construction efficiency of the bridge tower sections are improved.

The invention provides a method for acquiring the position and attitude of a model through a single camera, and belongs to the technical field of wind tunnel measurement. The method comprises the following specific steps: S1, installing a plurality of to-be-tested coding points on a model; s2, the model is adjusted to an initial attitude, so that the incidence angle, the roll angle and the sideslip angle of the model are all zero; s3, acquiring three-dimensional coordinates of a to-be-measured coding point in the wind tunnel coordinate system, shooting an image of the model by using the main camera, and acquiring two-dimensional pixel coordinates of the to-be-measured coding point in the image; s4, calculating a rotation matrix and a translation vector from a two-dimensional pixel coordinate to a three-dimensional coordinate; s5, moving the model, repeating the step S3, and calculating a rotation matrix and a translation vector from a two-dimensional pixel coordinate to a three-dimensional coordinate; s6, on the basis of the data in the step S4 and the step S5, calculating and acquiring pose information of the model and outputting the pose information; and S7, repeating the steps S5 and S6, and obtaining and outputting the pose information of the model in real time. The data obtained by the method provided by the invention is high in precision and more accurate.

The invention relates to the technical field of image registration, and discloses a point cloud and image registration method and system. The method comprises the steps that a calibration plate and a calibration plate coordinate system are constructed, the position of the calibration plate is changed, three-dimensional point clouds of a target object when the calibration plate is located at different positions are obtained through a laser radar, and two-dimensional images of the target object when the calibration plate is located at different positions are obtained through a camera; internal parameters and external parameters of the camera are determined through the calibration tool and the two-dimensional image; according to the three-dimensional point cloud, the two-dimensional image and the calibration plate, calculating a rotation matrix and a translation vector between a laser radar coordinate system and a camera coordinate system; projecting the three-dimensional point cloud to a camera coordinate system according to the rotation matrix and the translation vector to obtain a two-dimensional coordinate corresponding to the three-dimensional point cloud; and determining two-dimensional image pixels corresponding to the two-dimensional coordinates according to the two-dimensional coordinates, the internal reference and the external reference, and assigning the pixels of the two-dimensional image to the three-dimensional point cloud to obtain registration data. According to the invention, the accuracy of registration can be improved.

The invention discloses an unmanned aerial vehicle pose determination method and device, electronic equipment and a storage medium, and belongs to the field of unmanned aerial vehicle pose determination, and the method comprises the steps: synchronously obtaining a visible light image, a depth image and inertial data of an unmanned aerial vehicle, and extracting a vanishing point set from the visible light image, extracting a plane normal vector set and a feature point set from the depth image; based on geometric constraint conditions that the vanishing point direction is aligned with a preset orthogonal direction and the plane normal vector is perpendicular to the orthogonal direction, constructing a rotation matrix solving model, and optimizing a rotation matrix by taking minimization of a three-dimensional map point projection error as a target; and meanwhile, combining feature point depth information and an inertia pre-integration error, constructing a translation vector solution model for joint optimization, and finally fusing a rotation matrix and a translation vector to output the pose of the unmanned aerial vehicle. Therefore, by implementing the method, the problem of pose drift caused by lack of geometric constraints in the prior art can be solved.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a binocular structured light three-dimensional reconstruction and dynamic rotation matrix-based large rotation equipment rotor multistage stacking concentricity accurate measurement method, and belongs to the technical field of non-contact optical measurement, three-dimensional reconstruction and assembly precision measurement. The method comprises the following steps: 1, binocular structured light three-dimensional reconstruction; step 2, dynamic rotation matrix correction: correcting the space attitude of the surface of the spigot, and converting all measured point clouds to a unified coordinate system for concentricity calculation; 3, multi-stage stacking eccentricity error evaluation under the polar coordinate system is carried out, the concentricity is rapidly calculated on the basis of a three-dimensional reconstruction result, and visual distribution of eccentricity errors is formed for subsequent online compensation; and 4, constructing a feed-forward + feedback compensation closed-loop system based on the measurement and error transfer model in the step 3, and correcting the eccentric error in real time in the assembly process. The defects that existing contact measurement is low in efficiency, contact damage exists, and multi-stage assembly accumulative errors cannot be evaluated and compensated in real time are overcome.

The invention provides a processing method and system for scoliosis detection based on point cloud features. According to the system, a depth camera is used for monitoring the dynamic state of a subject through a point cloud data acquisition module, and back three-dimensional point cloud information is obtained; the scoliosis area acquisition module acquires a clustered two-dimensional diagram by using a density-based spatial clustering algorithm and a point cloud reprojection algorithm, and acquires an area of interest by using an image morphological processing algorithm and a polynomial curve fitting method; the point cloud registration module obtains symmetrical point clouds by using an iterative reweighted least square method and a principal component analysis algorithm, performs coarse registration by using a Rodrigues rotation matrix and then executes a point cloud registration algorithm; and the transverse asymmetry analysis module obtains a symmetry plane by using the registered point cloud, and obtains a transverse depth difference point cloud by using a surface reconstruction algorithm. The invention provides a transverse asymmetry analysis method based on a point cloud registration algorithm and a surface reconstruction algorithm, scoliosis screening is performed through the depth camera, and popularization of scoliosis screening is facilitated.

The invention discloses a surveying and mapping method and system based on an unmanned aerial vehicle remote sensing technology, and relates to the field of surveying and mapping. Comprising the following steps: unmanned aerial vehicle multispectral image data acquisition: carrying a multispectral camera at the bottom of an unmanned aerial vehicle, setting flight parameters of the unmanned aerial vehicle, and acquiring image data by the multispectral camera in the flight process of the unmanned aerial vehicle; image processing: carrying out radiation model correction and geometric correction on the acquired image so as to eliminate illumination variation and geometric distortion and ensure that the physical significance of image data is accurate; performing multi-view image feature matching, establishing a corresponding relation among different view images, and providing matching point pairs for three-dimensional reconstruction; and three-dimensional point cloud reconstruction: restoring a scene three-dimensional structure from the two-dimensional image, and generating a dense point cloud model. The method has the effect of improving precision, centroid centralization eliminates accumulative errors, covariance matrix construction accurately captures point cloud space distribution characteristics, singular value decomposition strictly ensures orthogonality constraint of a rotation matrix, and mathematical derivation can obtain a globally optimal solution.

The invention discloses an indoor scene point cloud blocking method, and relates to the technical field of point cloud processing. Comprising the steps of obtaining sparse point cloud data; calculating a first rotation matrix and an optimal second rotation matrix based on the sparse point cloud data; inputting single-frame point cloud data frame by frame, aligning the single-frame point cloud data to a Manhattan coordinate system through the first rotation matrix and the optimal second rotation matrix, and creating a sparse voxel grid; calculating a normal vector of each voxel center point based on the sparse voxel grid to obtain a normal vector set of the voxel center points; segmenting the sparse voxel grid by adopting an adaptive segmentation algorithm to obtain an initial point cloud block and recording a boundary value of the initial point cloud block; and according to the normal vector of each voxel central point and the boundary value of the initial point cloud block, expanding the initial point cloud block by using a region growing algorithm to obtain a point cloud block with a complete boundary target. The technical problem that a computer is difficult to read massive point cloud data is solved.

The invention discloses a multi-view three-dimensional reconstruction driven cattle body size online measurement method, and relates to the technical field of animal husbandry three-dimensional measurement, and the method comprises the steps: extracting a ground reference plane, a segment elevation function, a guardrail reference line, a segment guardrail spacing, a periodic pitch and a hoof ground contact event, and generating a calibration opportunity score in combination with a pixel-level quality weight map; in the sliding window factor graph, jointly solving an external parameter rotation matrix, an external parameter translation vector and a global scale by taking the prior as a constraint, and outputting an uncertainty matrix; implementing hierarchical self-recovery according to the comprehensive health degree and the score, and replacing safe writing with double buffer atoms; carrying out weighted splicing under a unified measurement coordinate and carrying out profile robust measurement, forwards transmitting uncertainty to a body size layer and supporting individual multi-time column-passing convergence; the method is suitable for channel visibility interruption and structure variable scenes, and under the condition of uninterrupted release, measurement consistency is kept and auditable results are output.

The application discloses a kind of bridge tower segment attitude intelligent prediction method and system based on multi-point cooperative positioning, method includes: S100, in the two adjacent sides of the segment that has been installed, select multiple observation points, using laser reflector obtains the space coordinates of observation point, using multiple total station instruments cooperates measurement;S200, based on rigid body transformation mathematical model, the attitude change prediction of segment to be installed, using rotation matrix and translation vector calculates the optimal attitude of segment to be installed, too much point redundant information optimizes attitude estimation, controls cumulative error;S300, BIM modeling is carried out to bridge tower structure, and high-precision three-dimensional rendering is carried out, complex monitoring data is converted into intuitive graphical interface, and segment state is shown in real time;S400, according to data comparison, combined with error analysis, if the error is larger than design data, then corresponding adjustment is carried out;S500, repeat the above steps, until all bridge tower segments are accurately positioned and installed. The precision and construction efficiency of bridge tower segment erection are improved.

The invention discloses a reasoning method based on a quantitative model, the quantitative model is obtained based on a trained model, the precision of at least part of parameters of the quantitative model is smaller than the parameter precision of the trained model, and the method comprises the following steps: obtaining input task data; inputting the input task data into the quantitative model to obtain an output result; the quantitative model comprises multiple layers of neuron sub-models, and for a first sub-model and a second sub-model of adjacent layers, the output activation value of the first sub-model is used as the input of the second sub-model; under the condition that a second sub-model of a specific adjacent layer of the quantitative model comprises a fusion weight matrix parameter, a specific rotation matrix is controlled to act on an output activation value of the first sub-model to obtain a modified activation value, and the modified activation value is smaller than the output activation value; the fusion weight matrix parameter is obtained by the specific rotation matrix acting on the second initial weight parameter of the second sub-model of the specific adjacent layer corresponding to the trained model.

One or more embodiments of the invention provide a model quantification method and apparatus, a storage medium and a program product. The method comprises the steps of reconstructing a weight of at least one network layer in a to-be-quantized initial model based on an orthogonal rotation matrix; performing quantitative perception training on the reconstructed model to jointly optimize the weight of the at least one network layer and the corresponding rotation matrix; the optimized weight is quantified to generate a target model, and the weight precision of the target model is lower than that of the initial model.

The invention discloses an intelligent processing method and system for multi-source data fusion of surveying and mapping equipment, and belongs to the technical field of surveying and mapping data processing.According to the method and system, an initial LiDAR point cloud is combined with position data and attitude data for joint calculation to obtain an accurate LiDAR point cloud, the accuracy of point cloud data is optimized, and therefore the accuracy of final registration is improved, and the registration accuracy is improved. And matching the point cloud multi-scale feature point set with the image multi-scale feature point set to obtain a point cloud homonymy point set and an image homonymy point set, analyzing a rotation matrix and a translation vector through a quaternion registration algorithm, and completing registration of the target panoramic image and the precise LiDAR point cloud according to the rotation matrix and the translation vector. And high-precision space registration is realized through a quaternion registration algorithm, the registration accuracy is further improved, and the surveying and mapping precision is improved.

The invention relates to the technical field of three-dimensional modeling, in particular to an electromechanical pipeline model construction system and method based on unmanned aerial vehicle oblique photography. The method comprises the following steps: collecting electromechanical pipeline images; extracting feature points from the image by using an SfM technology, matching a multi-view image, reconstructing a sparse point cloud, and obtaining an initial three-dimensional structure and a camera track of a scene; initializing the point cloud into a 3D Gaussian ellipsoid, and decomposing a covariance matrix through a rotation matrix and a scaling matrix to optimize calculation; projecting the 3D ellipsoid to an image plane based on the view transformation matrix and the Jacobian matrix to realize rasterization rendering; calculating an error between a rendered image and a real image by adopting a mixed loss function, optimizing Gaussian primitive parameters through back propagation, and performing adaptive density control at the same time; a high-precision three-dimensional model is generated, BIM or CAD format export is supported, and visual display can be performed in a VR / AR environment; according to the invention, intelligent management and monitoring of electromechanical pipelines are realized.

The invention provides a vehicle positioning method based on laser radar point cloud data. The method comprises the following steps: acquiring a source point cloud and a target point cloud; using an improved point-to-surface iterative nearest point algorithm to decentralize the source point cloud and the target point cloud, calculating a rotation matrix and a translation vector according to decentralized coordinates of the source point cloud and the target point cloud, and combining input disturbance and Lie algebra to obtain an initial registration conversion formula of the source point cloud; performing Gaussian Newton iteration on the initial registration conversion formula to obtain a registration conversion formula of the source point cloud, and performing registration on the source point cloud and the target point cloud to obtain registration point cloud data; and constructing a map by using the registered point cloud data, and positioning the vehicle. According to the invention, the technical problem that the algorithm precision is not high when vehicle positioning is carried out through point cloud data in the prior art can be solved.

An image processing method, an image display apparatus, a computer program product, and an aircraft are disclosed. The image processing method includes: generating a two-dimensional output image based on input information; guiding a light beam corresponding to the output image to a display unit; capturing a three-dimensional image displayed on the display unit; obtaining a photometric error and a geometric error between the captured three-dimensional image and the output image; constructing a loss function for the photometric error and the geometric error; solving the loss function through an algorithm for solving three-dimensional to two-dimensional point pair motion to obtain a rotation matrix and a translation matrix; and correcting the output image according to the rotation matrix and the translation matrix to display the corrected output image on the display unit.

The invention relates to a three-dimensional face recognition method for an underground coal mine, and belongs to the technical field of multi-dimensional face recognition. According to the method, the two-dimensional face image is converted into the three-dimensional points under the camera coordinate system, the rotation matrix is combined for rotation operation, the side face data of the rotating face at multiple angles are generated, and a rich data basis is provided for three-dimensional reconstruction. PointNet + + and an expansion structure thereof are combined with an auto-encoder frame to establish a three-dimensional reconstruction model, compact feature representation can be extracted from a three-dimensional point cloud, the features are utilized to reconstruct a three-dimensional face image with details and geometric structures, the three-dimensional face image is divided into a plurality of recognition areas, and feature recognition matching is performed respectively; during recognition, a face recognition result can be directly given only by screening clear areas for image reconstruction and feature matching. According to the invention, the accuracy and fidelity of reconstruction are improved.

The embodiment of the invention provides a multi-modal data perception fused large-scene space three-dimensional map reconstruction method. The method comprises the steps of performing registration on a first point cloud of a camera and a second point cloud of a radar to obtain an optimal rotation matrix and an optimal translation vector; correcting the initial pose of the camera according to the inverse matrix of the optimal rotation matrix and the reverse translation vector to obtain a corrected camera pose; converting a three-dimensional point of the first point cloud from a camera coordinate system to a world coordinate system according to the initial pose, and then converting the three-dimensional point of the first point cloud from the world coordinate system to the corrected camera coordinate system according to the corrected camera pose to obtain a first corrected point cloud; fusing the plurality of first correction point clouds to obtain a three-dimensional point cloud of the whole map; and performing surface reconstruction operation on the map point cloud according to a surface reconstruction algorithm. According to the embodiment of the invention, the camera can obtain more accurate point cloud data on the basis of keeping the long-distance advantage, so that a more accurate three-dimensional map model is obtained.