627 results about "Triangulation" patented technology

The invention discloses a multi-view panoramic point cloud splicing method, which comprises the steps of performing joint calibration on a binocular camera and a laser radar through a calibration algorithm, and aligning a coordinate system; a binocular camera is used for collecting a two-dimensional image and carrying out distortion correction, a depth map is generated based on parallax calculation, and three-dimensional point cloud reconstruction is carried out in combination with a triangulation principle; performing deep learning target detection on the two-dimensional image, and screening a line rod assembly area through non-maximum suppression; multi-view point cloud data are collected through a laser radar, point cloud segmentation processing is carried out, and a telegraph pole assembly point cloud subset is reserved; matching the two-dimensional pixel area of the telegraph pole assembly with a laser radar point cloud projection result, and screening point cloud data belonging to the telegraph pole assembly; and carrying out alignment and fusion on the multi-view point clouds through initial registration and fine registration by adopting an improved point cloud splicing algorithm to generate a panoramic point cloud image. According to the method, the point cloud splicing processing time is shortened, and the robustness, precision and integrity of point cloud splicing are improved.

The invention provides a three-dimensional modeling and mapping method, system and device based on a fixed-wing unmanned aerial vehicle, and relates to the technical field of geographic mapping. According to the method, through synchronous data acquisition of an inclined camera, a laser scanner and a position and attitude measurement system, and in combination with navigation trajectory calculation, point cloud coordinate transformation, image attitude calculation and multi-source data fusion processing, space collaboration of image data and point cloud data is realized under a unified coordinate system. And performing aerial triangulation based on the fused data, further carrying out spatial modeling and texture mapping, and effectively improving geometric consistency and modeling continuity among multi-source data. According to the method, ground control points do not need to be arranged, high-precision and high-consistency geographic space information expression can be achieved, and real scene modeling and multi-type surveying and mapping data result generation in a large-scale mapping scene are supported.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention discloses a construction site three-dimensional scene reconstruction method based on an unmanned aerial vehicle image and a monitoring video. The method comprises the following steps: acquiring a construction site scene multi-view image; a sparse three-dimensional point cloud and a camera pose are generated through a feature matching and motion recovery structure algorithm, and dense reconstruction is carried out to obtain a global three-dimensional point cloud; aligning the global three-dimensional point cloud with a world coordinate system by using geographic position information; estimating the position of a shooting camera in the three-dimensional point cloud, sampling a candidate view angle and rendering a virtual RGB image; based on two-dimensional feature matching of the shot image and the virtual RGB image, internal parameters and external parameters of the shooting camera are iteratively solved through triangulation and a pose optimization algorithm; monocular depth estimation is carried out on the shot image, and the shot image is converted to a measurement scale through static region depth alignment; and projecting the depth of the shot image to the three-dimensional point cloud, updating the dynamic object in real time, and fusing the dynamic object into a complete three-dimensional scene model. The method has the advantage that the real-time three-dimensional reconstruction of the dynamic scene of the construction site is realized.

The invention discloses a shock wave overpressure field global measurement method based on multi-view image fusion, belongs to the technical field of explosive shock wave measurement, and is suitable for weapon equipment power evaluation and blasting safety analysis. The method comprises the following steps of: synchronously acquiring a time sequence image of the whole explosion process through a distributed multi-view high-speed imaging system, preprocessing the image by adopting pixel-by-pixel comparison, square operation enhancement and normalization processing, and positioning a shock wave edge contour; the blasting center coordinate is positioned through binocular parallax, the shock wave initial radius is determined by combining spatial domain analysis, and the key feature points of the front and rear edges of the wavefront are detected by using a dynamic search window and a radial gradient field. Based on multi-view constraints, a three-dimensional point cloud is generated through direct linear triangulation, and a three-dimensional wave front form is reconstructed in combination with least square spherical fitting. And finally, constructing a wavefront radius-time evolution model, and deducing a quantitative relationship between the instantaneous propagation velocity and the peak overpressure in combination with a Ranki ne-Huton iot relationship, thereby realizing the global high-precision calculation of the overpressure field.

The invention discloses an unmanned aerial vehicle camera attitude estimation optimization method and device, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: acquiring data by using an unmanned aerial vehicle carrying various sensors, preprocessing, carrying out feature extraction and matching on preprocessed image data, removing mismatching points, and based on an initial point cloud, carrying out sparse reconstruction in an incremental expansion and bundle adjustment optimization stage; integrating the re-projection error term, the flight path constraint, the epipolar geometric constraint and the triangulation constraint into a target function; constructing a model for predicting the pose correction based on the deep residual convolutional neural network, and taking the initial pose of the camera and the corresponding local image as input; and adjusting the weight of each constraint in the target function based on the predicted pose correction and pose confidence, and minimizing the target function to improve the precision of unmanned aerial vehicle camera pose estimation. The problem of reconstruction scene deviation caused by inaccurate camera attitude estimation in the prior art is solved.

The invention discloses a three-dimensional vision calibration and positioning method and system based on an event camera, and belongs to the field of positioning. The method comprises the steps of constructing an array containing a plurality of unique frequency coding light sources, constructing a binocular event camera system, collecting light source array light signal event streams, extracting pixel positions, frequencies and timestamp information, determining pixel coordinate centroids of the light sources through frequency and pixel clustering analysis, and establishing frequency and centroid mapping. Auxiliary light sources with the same coding rule are arranged on the surface of a target object, and after a binocular system collects light signals of the auxiliary light sources, the center-of-mass coordinates of the light sources are matched according to the mapping relation. And based on event camera calibration parameters and binocular external parameters, correcting and stereoscopically matching centroid coordinates, and calculating three-dimensional coordinates of the auxiliary light source through triangulation. And in combination with the known geometric topological relation of the auxiliary light source on the surface of the object, the spatial pose of the target object is solved by using a rigid transformation algorithm, and high-precision positioning is completed. According to the embodiment, the calibration precision can be improved in a complex application environment.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention relates to the technical field of intelligent maintenance of tunnel electromechanical facilities, in particular to a tunnel lamp recognition and spatial positioning method based on binocular vision, which comprises the following steps: synchronously acquiring tunnel environment images through left and right cameras subjected to polar horizontal correction, inputting the images into a lightweight tunnel lamp detection model, and detecting an area image containing a lamp bounding box; extracting feature points in the bounding box by adopting a multi-scale adaptive FAST feature extraction algorithm, describing the feature points by rotating an invariant binary descriptor, and generating a disparity map in combination with an adaptive Hamming distance matching algorithm; calculating three-dimensional space coordinates of the lamp according to the disparity map by using a binocular camera triangulation principle and combining camera parameters; when the ambient illumination is lower than a threshold value, a multispectral image fusion module is started to enhance the feature matching robustness; and when continuous matching fails, visible light communication assisted positioning is started. According to the invention, the problem of low positioning precision of the lamp in a complex tunnel environment is solved, and the reliability and efficiency of identification and positioning are improved.

The invention discloses a hot-dip galvanized steel pipe ground cage verticality measuring system based on machine vision, and relates to the technical field of physical measurement, and the system comprises a projection unit which is used for projecting polarization gray scale stripes with polarization codes to a to-be-measured hot-dip galvanized steel pipe ground cage; the imaging unit comprises at least two cameras which form a triangulation base line with the projection unit so as to synchronously acquire multi-view images from different views; and the processing unit is configured to perform polarization difference fusion on the multi-view images to suppress mirror reflection, generate corresponding polarization difference fusion images, reconstruct pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages, generate pipe body axial vectors by adopting robust cylinder axis fitting, and obtain the pipe body three-dimensional point clouds of all the hot-dip galvanized steel pipes in the corresponding hot-dip galvanized steel pipe ground cages. And calculating the perpendicularity deviation angle of the pipe body according to the axial vector of the pipe body and the gravity direction. Therefore, on the premise that the measurement precision is guaranteed, the spatial postures of the multiple steel pipes are synchronously collected and recognized at a time, multi-point repeated scanning is not needed, and the detection efficiency is remarkably improved.

The invention is suitable for the technical field of radar signal processing, and provides a space target SFM three-dimensional reconstruction method based on a multi-frame ISAR image, and the method comprises the steps: carrying out the preprocessing and imaging of large-angle inverse synthetic aperture radar ISAR echo data of a space target, and obtaining a multi-frame ISAR image sequence; extracting scattering points in each frame of ISAR image sequence according to an orthogonal matching pursuit algorithm, and performing feature matching on adjacent frames of ISAR image sequences to obtain a scattering point set; obtaining instantaneous radar sight line information, and constructing a projection matrix from a space target scattering center three-dimensional coordinate to an ISAR two-dimensional pixel plane according to the instantaneous radar sight line information; and obtaining a three-dimensional reconstruction result according to the scattering point set and the projection matrix in combination with a triangulation principle in an incremental motion recovery structure SFM method. According to the method, the robustness and precision of three-dimensional reconstruction can be improved.

The invention discloses a space-ground-air integrated multi-source data acquisition and fusion method based on highway facilities, and relates to the field of data acquisition and fusion, and the method comprises the steps: obtaining the three-dimensional geographic information data of the highway facilities based on a diversified data acquisition technology, and carrying out the fusion processing of the three-dimensional geographic information data through employing a data fusion registration algorithm, outputting multi-source data information; verifying the fusion precision of the multi-source data information by using the elite centroid and a reverse learning strategy, and performing secondary fusion processing of the three-dimensional geographic information data according to a verification result to obtain a multi-source data fusion result; and performing triangulation network reconstruction processing on the multi-source data fusion result based on a triangulation algorithm, and performing visual display on the reconstruction result, the multi-source data fusion result and the three-dimensional geographic information data by using a three-dimensional digital display technology. According to the invention, multi-source point cloud processing software is used to carry out precision check and other operations on the fused point cloud, so that the integrity and smoothness of the point cloud data are ensured.

The invention discloses a multi-target tracking method, system and device, a medium and a product, and relates to the technical field of computer graphic vision, and the method comprises the steps: based on a plurality of single-view two-dimensional images, employing a lightweight key point detection model to extract human body two-dimensional key point coordinates under different views; based on the camera parameters corresponding to different visual angles and the human body two-dimensional key point coordinates under different visual angles, performing three-dimensional reconstruction on the human body key points by using a triangulation method to obtain a three-dimensional reconstruction model; projecting the three-dimensional reconstruction model, and determining a projection frame corresponding to each target in the tracked area; and a tracking algorithm and Kalman filtering are combined to complete real-time tracking of a plurality of targets in the tracked area. According to the method, the three-dimensional target tracking problem is reduced into the two-dimensional target tracking problem based on the projection frame of the three-dimensional reconstruction model relative to the horizontal plane, the accuracy and robustness of the model can be improved in a multi-target tracking scene, and meanwhile, the calculation amount is reduced.

The invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

The invention relates to the technical field of three-dimensional geological modeling and geological mapping data processing, in particular to an oblique photography three-dimensional surface model geological modeling system and method, and the system comprises a data collection module, a scene construction module, a boundary line processing module, a geologic body modeling module, a profile linkage module and a result output module. The data acquisition module fuses the oblique photography image and topographic data, and generates a three-dimensional earth surface model through a motion recovery structure algorithm; the geologic body modeling module constructs a closed geologic body through implicit curved surface reconstruction and constrained triangulation; a profile linkage module calls a graph neural network to predict a stratigraphic intersection line trend, and realizes two-dimensional and three-dimensional real-time synchronization based on event driving and sparse voxel hash mapping; and the result output module executes data verification and generates a multi-dimensional geological report. According to the scheme, dependence of a third-party tool is eliminated, geological mapping efficiency is improved, and the problem of section linkage delay is solved.

The invention relates to the technical field of camera testing, in particular to a camera triangulation distance measurement detection method. Comprising the following steps: selecting a tested camera, and adjusting the position and angle of the camera; according to the positions of the two targets in the image, setting test areas corresponding to different view field angle pictures, and adopting an area detection mode; angular point detection is carried out on the set area, the angular points and sub-pixel-level angular points are obtained, and checkerboard center angular points are judged and corrected; according to a distortion calibration model, selecting a corresponding function to carry out distortion removal calculation on the angular point to obtain a normalized distortion-removed angular point coordinate, and converting the normalized distortion-removed angular point coordinate into an actual distortion-removed pixel coordinate; calculating the calculation distance of two target angular points of each image, establishing a normalized three-dimensional coordinate, calculating the deviation angle of the left and right angular points by using the three points, and combining the measured parameters to obtain the calculation distance of the two angular points; and calculating a precision error. According to the technical scheme, the accuracy of the internal reference calibration data and the reliability of the distortion model can be effectively verified.

The invention discloses a submarine topography reconstruction system and method based on optical mobile measuring and scanning, and relates to the technical field of ocean exploration, and the system comprises the following units: an image collection unit, which is used for coordinating the emission of 532nm pulse laser and the collection of a gating camera image through a synchronous sequential circuit, a triangulation measurement optical structure with separated emission and reception is adopted, and a submersible can be carried for mobile measurement and scanning; the system calibration unit is used for carrying out joint calibration on camera parameters and a laser plane so as to determine system calibration parameters, so that the spatial position of a laser stripe accurately corresponds to the actual coordinate of the target surface; and the image processing unit is used for preprocessing the image acquired by the system and further extracting the coordinates of the center line of the laser stripe in the preprocessed image. According to the invention, adjacent splicing can be carried out through continuously collected laser stripe images to construct two-dimensional submarine landform information, and three-dimensional reconstruction of submarine topography can be carried out by using generated point cloud data.

The invention provides a short-baseline binocular three-dimensional human body posture reconstruction method and system, and the method comprises the steps: collecting original left and right image pairs, and carrying out the detection of a human body region; carrying out human body image pair matching and scale transformation on the detected human body region; parallax features are obtained based on the processed left and right human body area image pairs, feature fusion is carried out on the parallax features and human body features extracted by the three-dimensional human body posture estimation sub-network, a parallax image initial result is obtained based on feature fusion information, a loss function is established with the human body features to carry out parallax optimization, and an optimized parallax image and a scene depth image are obtained; human body features are obtained based on left and right human body area image pairs, feature fusion is carried out on the human body features and parallax features, two-dimensional human body joint points are obtained through two-dimensional posture estimation according to feature fusion results, and a three-dimensional human body posture initial result is obtained based on binocular camera parameters and a triangulation calculation method; and establishing a loss function with the scene depth map and human body prior supervision, and performing three-dimensional human body posture optimization to obtain an optimized three-dimensional human body posture.

The invention relates to a point cloud data filtering method and system based on a progressive triangulation network, and a medium, and belongs to the technical field of point cloud data processing. The method comprises the following steps: projecting point cloud data to the same plane; moving the rectangular window to enable the rectangular window to traverse all the point cloud data so as to extract undetermined ground seed points; screening to-be-determined ground seed points through curved surface fitting, removing non-ground seed points, and obtaining final ground seed points; and establishing an initial triangulation network, calculating a repeated distance and a repeated angle between the unclassified points and the initial triangulation network, determining an encrypted triangulation network, and obtaining ground points. According to the method, more to-be-determined ground seed points are extracted by sliding the rectangular window, the ground points are prevented from being mistakenly rejected through multiple times of curved surface fitting and seed point screening, the noise point rejection effect is improved, the method adapts to complex terrains, and the filtering precision is improved by encrypting the initial triangulation network.

The invention provides a cross-source fusion modeling method and system for a forest three-dimensional point cloud model. The method comprises the following steps: acquiring first information based on camera equipment carried by an unmanned aerial vehicle, wherein the first information comprises tree canopy layer image data above a preset height of a forest and forest underlayer image data below the preset height of the forest; performing image processing based on the first information, and preprocessing point cloud data obtained by processing to obtain preprocessed point cloud data; determining canopy layer forest data and understory forest data based on the preprocessed point cloud data, and obtaining position data of each forest; and carrying out primary registration on the position data of each forest based on an improved Delou triangulation network algorithm, and carrying out secondary registration on a primary registration result through an optimization function to obtain a forest three-dimensional point cloud model. According to the method, the efficiency and precision of forest three-dimensional modeling are remarkably improved.

The invention relates to the technical field of data processing, and provides a BIM model continuous grid bottom surface extraction method, device and equipment and a storage medium, according to the scheme of the invention, grid bottom surface extraction is carried out based on a user-defined data structure after BIM model triangularization, the influence of a professional design software production environment and a multi-source heterogeneous storage format is eliminated, and universality is achieved; according to the method, the cleaned and corrected triangularized grid model is subjected to space division and an elevation extraction algorithm, so that the grid bottom surface extraction efficiency is greatly improved; according to the method, curved surface reconstruction is carried out according to the spatial point cloud normal set and the point cloud data set, so that the construction accuracy of grid bottom surface extraction in the projection direction is ensured, a finer and more continuous geometric expression is provided, and the problem of model bottom surface image missing is solved.

The invention relates to the technical field of three-dimensional digital modeling, in particular to an automatic real-time three-dimensional digital modeling method and system for a dike. The method comprises the following steps: collecting first image data of a dike area, and setting a three-dimensional coordinate system of the dike area; evaluating a plane median error and an elevation median error of the first image data, and obtaining field acquisition precision based on the plane median error and the elevation median error; adjusting shooting parameters of the unmanned aerial vehicle according to the field acquisition precision, and acting the adjusted shooting parameters of the unmanned aerial vehicle on the unmanned aerial vehicle; acquiring second image data of the dike area through the adjusted unmanned aerial vehicle; executing spatial triangulation calculation on the second image data, and generating aerial triangulation calculation result data; and performing three-dimensional model reconstruction based on the aerial triangulation result data to obtain a dike three-dimensional digital model. According to the method, the frequency and the precision of obtaining the geometric information of the dike can be improved, and high-resolution and dynamically-updated three-dimensional modeling is realized.

The present disclosure relates to an apparatus and a method for providing a plurality of motion vectors related to an image coded in a bitstream. The method includes obtaining a set of sample positions within the image, obtaining respective motion vectors associated with the set of sample positions, deriving an additional motion vector based on information coded in the bitstream, determining an additional sample position located within a triangle, which is formed by three sample positions of the set of sample positions, based on the respective motion vectors associated with the three sample positions, the triangle not including the rest of the sample positions of the set of sample positions, adding the additional sample position to the set of sample positions, and associating the derived additional motion vector with the additional sample position.

The invention provides an airborne point cloud building contour point extraction method based on a constraint triangulation network and semantic ring analysis, and belongs to the field of airborne laser radar data processing, and the method comprises the steps: obtaining an airborne laser radar point cloud data set of a to-be-processed region; performing classification processing by using a filtering algorithm to obtain a surface point set and a non-surface point set; building candidate seed points are extracted, and building point clouds are extracted; a Delaunay triangulation network is constructed based on the building point clouds, and constraint is carried out based on the distance between the point clouds; constructing a concurrent triangle set, and extracting contour points and contour connecting edges; and constructing a topological graph, performing traversal search on the topological graph, determining the minimum inclusion rectangle of the closed loop, and judging the contour point type corresponding to the closed loop according to the geometrical shape parameter and the spatial distance parameter of the minimum inclusion rectangle. According to the method, the triangulation network is constructed by using the disordered contour points, and the sequence relationship and semantic information between the points are considered, so that accurate extraction of the semantic contour points is realized.