68 results about "Geometric matching" patented technology

The invention, which relates to the technical field of image processing, discloses a city component automatic three-dimensional reconstruction method based on a sub-meter high-resolution remote sensing image, comprising the following steps: acquiring a sub-meter high-resolution remote sensing image of a target area, and establishing an initial ground-image projection mapping relation based on a rational polynomial coefficient model; acquiring a ground control point, correcting the mapping relation, and outputting a registration image; performing multi-view geometric matching on the registered image to generate dense earth surface point cloud, and inputting the dense earth surface point cloud into a semantic neural radiation field network to generate dense point cloud; performing cross-modal feature alignment and fusion on the dense point cloud and the vector data, and outputting a fused multi-source dense point cloud; according to the method, through fine correction of the multi-source remote sensing image, the problems of large registration error, more point cloud sparse noise and semantic deficiency are solved, and high-precision and automatic three-dimensional reconstruction of urban components is realized.

The invention relates to the field of robot control, and discloses a robot control method and system, and the method comprises the steps: carrying out the fusion sensing of environment data through a multi-modal sensor, and constructing a dynamic environment model through the combination of the clustering segmentation of a dynamic obstacle and the geometric matching of a target cart; generating a Nash equilibrium obstacle avoidance path based on a non-cooperative game framework, and coordinating motion tracks of multiple robots by using distributed model predictive control and an alternating direction multiplier method; a heterogeneous grabbing pose generation network is designed, simulation and real scene feature distribution are aligned through domain adversarial training, and online optimization of a grabbing strategy is achieved in combination with incremental learning; newly-added obstacles and cart deviation are monitored in real time to trigger dynamic re-planning, and the path and the grabbing pose are updated through a closed-loop feedback mechanism. According to the method, the cooperative control robustness in a dynamic environment is improved, the obstacle avoidance efficiency and the grabbing stability are both considered, and the method is suitable for automatic operation in complex scenes such as airports.

The invention discloses a method for reconstructing an ultrahigh-resolution pathological full slice. The method comprises the following steps: acquiring an effective tissue area image of a pathological tissue fragment by utilizing a fusion mask; an edge classification model and an image enhancement strategy are adopted to obtain an effective splicing edge of the effective tissue area image; constructing a multi-dimensional matching scoring system to calculate a geometric matching degree of a splicing edge, and screening out an optimal Top-N splicing scheme in combination with splicing quality evaluation of a deep neural network; and based on a genetic algorithm, combining global topological optimization, sub-pixel level fine tuning and a multi-dimensional matching scoring system to evaluate splicing quality, realizing iterative stitching of the effective tissue area image, and finally outputting an optimal splicing scheme. Reconstructing a full-slice image of the pathological tissue based on the optimal splicing scheme; the method has efficient and accurate automatic splicing and iterative optimization capability, and rapid reconstruction of the ultrahigh-resolution pathological full slice is realized.

The invention discloses an industrial automobile crane positioning and mapping method and device based on multi-sensing fusion, and relates to the field of machine sensing, and the method comprises the following steps: initializing a pose and offset of an inertial measurement unit by using an input laser point cloud and a pre-integration result of the inertial measurement unit; correcting laser point cloud motion distortion by using an inertial measurement unit, extracting a dynamic object point cloud, and synchronously extracting laser features and visual features with depth; calculating a key frame pose in real time by minimizing a visual re-projection error and an inertial measurement unit pre-integration error, and performing frame-to-map matching by taking the key frame pose as an initial value to further optimize the pose; a visual word bag model is utilized to quickly retrieve a closed-loop candidate frame, after laser passes geometric matching to verify a closed loop, a laser radar factor, a visual factor, an inertial measurement unit factor and a closed-loop factor are fused, a global factor graph is constructed and optimized, and a pose and point cloud map is updated. According to the method, the environment sensing capability of the industrial automobile crane can be improved, and the sensing accuracy and robustness are improved.

The invention relates to the technical field of motor manufacturing, and discloses a motor stator and rotor iron core sheet stamping precision detection system. The detection system comprises an image acquisition module which is used for acquiring stamping area image data including a lamination contour, a punching position and a surface appearance; and the deformation detection module is used for carrying out dynamic compensation strategy processing on the image data, inputting a three-dimensional reconstruction processing layer to analyze errors and generating deformation characteristic data. A preprocessing unit of the three-dimensional reconstruction processing layer calibrates blocks and balances gray levels, a feature analysis unit carries out joint modeling based on historical data, a geometric matching layer aligns data, a difference quantization layer models morphological differences, and a compensation decision-making layer carries out fusion to generate deformation feature data. The detection method corresponds to the method. The punching precision of the iron chip can be accurately and efficiently detected, detection is optimized through historical data, the method is suitable for the complex iron chip, good universality and expansibility are achieved, and the motor manufacturing quality and efficiency are improved.

The invention discloses a physical object three-dimensional positioning method and system based on video data, and relates to the technical field of three-dimensional positioning. The method is used for solving the problems of low object space positioning precision, unstable pose estimation and poor time sequence continuity in a video scene. Firstly, an input video stream is analyzed, an object segmentation mask is generated, feature points are extracted, camera motion parameters are calculated through inter-frame matching, and a scene sparse three-dimensional point cloud is reconstructed; a candidate three-dimensional bounding box is generated according to the segmentation mask and the point cloud, an optimal bounding box is selected by combining geometric matching degree and feature similarity evaluation, and a preliminary three-dimensional positioning result of the object is obtained; a positioning result and historical frame motion data are fused, a space-time constraint optimization model is constructed, and the six-degree-of-freedom pose of the object is solved; and finally, neural radiation field representation is established based on the pose, the pose and neural parameters are optimized through combination of micro rendering and back propagation, continuous and accurate updating of the three-dimensional position of the object is realized, and the positioning stability and robustness in a dynamic scene are improved.

The invention provides a Bayesian fusion-based 4D millimeter wave radar point cloud enhancement and target detection method, and the method comprises the steps: 1, projecting a 4D millimeter wave radar point cloud to an image plane, carrying out the matching of an image instance segmentation result, and obtaining an image semantic category and image segmentation confidence; 2, constructing a class conditional probability density model based on the physical characteristics of the radar points; based on a Bayesian probability framework, carrying out weighted fusion on the image segmentation confidence and the class probability density to obtain fusion confidence; 3, taking the fusion confidence and semantic category codes as augmented features, and performing feature splicing on the augmented features and the 4D millimeter-wave radar point cloud to generate a semantic enhanced point cloud; and sampling the semantic enhanced point cloud by adopting a sampling method fusing confidence guidance, and inputting the sampled semantic enhanced point cloud into a detection network for training and reasoning. According to the method, the technical problems of unidirectional dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology are solved.

The invention belongs to the technical field of building construction and digital twinning, and relates to an intelligent matching method and system based on artificial intelligence. According to the method, construction environment data are collected in real time, risk marks are identified, the deformation amount of the component is calculated in combination with a time sequence prediction model, and a BIM model is corrected to generate a virtual component for environment compensation; carrying out geometric matching analysis by adopting a three-dimensional point cloud registration technology, and judging a hole alignment error and an interface gap; dynamically adjusting the installation sequence based on the dynamic decision tree model by integrating the geometric mismatch degree, the environmental risk and the construction logic; and carrying out incremental updating on the model according to the actual installation deviation. According to the method, the technical problem of installation mismatch caused by component deformation due to environmental factors such as temperature and humidity in the construction process is effectively solved, dynamic adaptation and precision control of the construction process are achieved, the matching success rate and construction efficiency of component assembly are improved, and meanwhile the adaptability and reliability of the system are optimized through continuous learning.

The invention discloses a small sample non-uniform multi-mode clutter modeling and partition suppression method, which is applied to the technical field of radars, performs multi-mode clutter modeling aiming at a complex environment, partitions the non-uniform clutter environment based on the difference of non-uniform clutter covariance matrix geometric matching, and improves the robustness of the multi-mode clutter modeling. Then, the problem that the performance of a traditional clutter suppression method is reduced under the small sample condition is solved through a dimensionality reduction self-adaptive filtering method in different areas; according to the method, firstly, multi-mode clutter echoes are generated for a complex environment, different-mode clutter region boundaries are judged through the difference of different clutter covariance matrix geometric matching, clutters are partitioned, and then the degree of freedom is reduced through data dimension reduction in different regions, and filter weight vectors are designed to achieve self-adaptive filtering; according to the method, effective partitioning and suppression of multimode clutters can be realized under the conditions that the number of samples is less than the degree of freedom of a radar system and the clutter environment is not uniform.

The invention discloses an intelligent blanking method for manufacturing a novel composite material for a retired wind power blade, and relates to the technical field of signal processing. The method comprises the following steps: acquiring geometric data of a blade and degradation characteristics of an internal material through a three-dimensional laser scanning and nondestructive testing technology, and generating a high-precision material non-uniformity distribution map; calculating a fiber feature matching degree and a stress buffer margin based on the atlas in combination with performance requirements of the composite material, and generating a reinforced phase distribution scheme; a geometric matching degree and a cutting angle compensation value are calculated according to the scheme, and a cutting track with optimized stress distribution is planned; the cutting equipment is driven to execute the track, the acoustic emission vibration spectrum is monitored in real time, and a control instruction containing a self-adaptive speed reduction mechanism is generated; and performing closed-loop feedback on the quality data based on the blanking unit to the atlas construction step. According to the method, the material utilization rate maximization, fiber continuous protection and defect active avoidance are realized, and the performance stability and the production efficiency of the composite material are remarkably improved.

The invention relates to the technical field of medical image processing and computer aided design, in particular to a preoperative CT-guided thoracic stent parameterization customization system, which comprises an image processing module for acquiring and preprocessing a patient trachea CT image; the contour segmentation module is used for carrying out trachea contour segmentation on the preprocessed CT image and carrying out correction to obtain trachea three-dimensional contour data; the parameter extraction module is used for extracting morphological parameters of the trachea; the modeling optimization module is used for establishing a stent model according to the morphological parameters of the trachea, constructing a comprehensive objective function which aims at minimizing the geometric matching error of the stent and meeting the mechanical property of the stent, and optimizing the stent model based on the objective function to obtain an optimal stent model; and the parameter output module is used for outputting support design parameters. According to the method, the customization precision and efficiency of the thoracic cavity stent are improved, and the influence of subjective experience factors in a traditional customization method is reduced.

The invention discloses an unmanned aerial vehicle landslide image clustering method and device for emergency monitoring, and belongs to the technical field of landslide monitoring. The method comprises the following steps: acquiring an original image shot by a camera mounted on an unmanned aerial vehicle; extracting multi-modal features of the landslide instance from the original image, wherein the multi-modal features comprise a spatial position feature, a global semantic embedding feature and a local geometric matching feature; fusing the multi-modal features by using a neural network model based on an attention mechanism to generate an instance feature vector; on the basis of the spatial position features, the local geometric matching features and the instance feature vectors, through an iteration process including a feedback mechanism, a graph structure representing the incidence relation between the landslide instances is constructed; and connected components in the graph structure are extracted, and each connected component is determined as a landslide instance cluster. According to the invention, different image instances belonging to the same landslide can be accurately associated, and the error rate of cross-frame target matching under a complex terrain is reduced.

The invention relates to a vehicle-mounted refrigerator welding control method, device and equipment and a storage medium, and the method comprises the steps that a welding identification code of a vehicle-mounted refrigerator fixed to a preset welding station is determined, and three-dimensional geometric data and surface mark point information of the vehicle-mounted refrigerator are determined; welding positioning data of a welded junction are determined through a geometric matching algorithm to adjust the pose of a welding gun; the welded junction is welded according to the first welding data, state information of the welded junction is collected in real time, and welding operation is adjusted according to the state information of the welded junction; when the welded junction meets the preset welding quality threshold value, a welding ending instruction is correspondingly generated to adjust the posture of the welding gun, and welding parameters corresponding to the welding operation are bound with the welding identification code to obtain second welding data; and the first welding data are updated according to the second welding data, and the vehicle-mounted refrigerator is moved out of the welding station. High precision, high stability and whole-process traceability of the welding process of the vehicle-mounted refrigerator are achieved, and the welding quality and the production efficiency are remarkably improved.

The present invention provides an affine motion estimation method for a dynamic point cloud. The method comprises the following steps: (1) point cloud preorder motion analysis: reading a point cloud sequence to be processed, estimating an affine motion field of the current point cloud starting from a third frame, and performing, on a point cloud having undergone preorder decoding, inter-frame geometric motion analysis to obtain motion prior information; (2) point cloud subset division based on the motion prior information; and (3) affine motion estimation of point cloud subsets: establishing a geometric matching relationship between a reference point cloud subset and the current frame, obtaining an affine transformation matrix between a reference frame subset and the current frame on the basis of singular value decomposition, and iterating all the point cloud subsets to complete affine motion estimation from reference frames to the current frame. The method of the present invention further improves the compression performance of a dynamic point cloud.

The invention discloses an apple three-dimensional reconstruction method and device under a limited visual angle, a storage medium and equipment, and the method comprises the steps: obtaining a multi-visual-angle image which corresponds to a to-be-reconstructed apple and only covers a part of a visible surface, and carrying out the three-dimensional reconstruction of a visible surface region of the apple based on the multi-visual-angle image through a three-dimensional reconstruction model, determining a target three-dimensional point cloud of the apple; extracting a local theoretical point cloud corresponding to the target three-dimensional point cloud from the Apple theory geometric model, and performing space alignment on the local theoretical point cloud and the target three-dimensional point cloud; according to a spatial alignment result, constructing a geometric matching error function between the local theoretical point cloud and the target three-dimensional point cloud, and based on the geometric matching error function, optimizing the Apple theoretical geometric model by taking reduction of geometric matching errors as an optimization target so as to obtain an optimized Apple theoretical geometric model; and optimizing the three-dimensional reconstruction model by using the optimized apple theoretical geometric model, and performing three-dimensional reconstruction covering the complete surface of the apple.

The application discloses a method and system for inverting a structure of a seismic-induced soft and hard interbedding anti-inclined ancient landslide stratum, and relates to the technical field of seismic-induced landslide simulation and prevention, which comprises the following steps: reconstructing a pre-destabilization topography to obtain a geological profile of a main sliding direction before destabilization; constructing a plurality of soft and hard interbedding anti-inclined slope models with different soft and hard interbedding thickness ratios by using a discrete element numerical simulation technology based on a damage theory, and then simulating a dynamic failure process to obtain a characteristic line of a simulated slope failure surface; determining an actual topographic line based on unmanned aerial vehicle surveying and mapping data, and calculating a geometric matching degree according to the actual topographic line and the characteristic line of the simulated slope failure surface; selecting a soft and hard interbedding anti-inclined slope model with the largest geometric matching degree, and marking a corresponding soft and hard interbedding thickness ratio as a parameter of a seismic-induced pre-destabilization soft and hard interbedding anti-inclined ancient landslide stratum structure. The application can calculate the parameter of the seismic-induced pre-destabilization soft and hard interbedding anti-inclined ancient landslide stratum structure which cannot be directly obtained through field investigation.

The present invention belongs to the field of computer vision technology and relates to a point cloud registration method based on plane primitives in a structured point cloud scene. The method relies on a pre-processing module in the system to filter and downsample to output point cloud information, a plane extraction module to extract planes with obvious features, and a plane matching module to search for corresponding surfaces; a transformation matrix calculation module calculates the transformation matrix of the successfully matched plane features, and an optimal selection module selects the transformation matrix with the highest registration accuracy. The method filters, downsamples, and segments the source point cloud and the target point cloud to be registered to obtain planes, calculates plane feature descriptors and angle feature descriptors and performs geometric matching based on them, obtains a set of successfully matched planes and calculates the transformation matrix, selects the transformation matrix with the highest point cloud overlap to perform point cloud registration and quantization, thereby realizing point cloud registration in a structured scene. The method improves the accuracy of point cloud registration in structured scenes and reduces the running time of point cloud registration.

The invention provides a laser scanning historic building structure data acquisition method and system, relates to the technical field of laser scanning, and can calculate the scanning range of a scanner in real time by obtaining a preset key monitoring area in a historic building and the real-time spatial position and attitude information of the laser scanner. On the basis, a three-dimensional bounding box intersection algorithm is adopted to carry out real-time space geometric matching calculation on the scanning range and the key monitoring area, so that whether an overlapping relation exists or not is judged. And intelligently marking the collected point cloud data as high-value data or common-value data according to the overlapping relationship. According to the method, the high-value data is written into the high-priority cache queue, the common-value data is written into the common-priority cache queue, and differentiated cache management and unloading strategies are executed on the data in the two queues, so that the dynamic monitoring task of the historic building can be effectively carried out; and the problem that the model loses the analysis value due to data missing or timestamp disorder is avoided.

This invention discloses a cross-view positioning method based on semantic coarse localization and BEV fine registration, aiming to solve the problem of large-scale, high-precision global positioning under conditions of no initial position prior. The invention employs a two-stage processing architecture: First, hierarchical semantic information is extracted from multi-view images from the vehicle using a visual language model. Combined with heading constraints and a backtracking retrieval strategy, candidate regions are quickly screened in a standard map to achieve semantic coarse localization. Then, within the candidate regions, the vehicle-mounted BEV perception results are finely geometrically registered with the map template, and a dynamic weight fusion mechanism based on road and building responses is introduced. The fusion weights are adaptively adjusted according to the degree of support from building structures to complete the fine registration. This invention organically combines high-level semantic information with low-level geometric information, achieving high-precision and robust cross-view geolocation even in the absence of coarse position prior. It effectively solves the problems of easy confusion in pure geometric matching, low accuracy in pure semantic localization, and poor adaptability of fixed fusion methods, making it suitable for global positioning of unmanned systems in complex urban environments.

The invention discloses a water conservancy drawing auditing method and system based on vector analysis and semantic fusion, and relates to the technical field of intelligent water conservancy drawing auditing. The method comprises the steps that a vector file of a hydraulic engineering drawing is analyzed, line segment endpoint coordinates are extracted to construct a graphic information set, and size marking entities are extracted to construct a size marking set; redrawing the vector line segment into a scalar image based on the endpoint coordinates, and outputting a confidence coefficient matrix by using a pre-trained encoder-decoder deep convolutional neural network; in combination with the mapping relation and the confidence coefficient matrix, screening out a target line segment of the target building through a classification decision algorithm; and judging whether the target line segment has an effective size mark or not by utilizing a geometric matching algorithm based on the geometric attribute, and carrying out differential marking on a mark missing object. According to the method, the problems of low identification precision and inaccurate missed mark detection caused by vector information loss due to rasterization and lack of geometric logic verification in the prior art are solved, and high-precision inspection of the water conservancy drawing is realized.

This invention discloses a mine production process monitoring and management system and method based on digital twins, belonging to the field of monitoring and management technology. It includes: a geometric matching module for obtaining matching results based on the geometric information of an underground centralized transfer station; a dust cluster identification module for comparing the dust quality of all internal dust areas in the underground centralized transfer station based on the matching results to obtain the main dust cluster area; a gap screening module for screening gap areas adjacent to the main dust cluster area to obtain the main injection gap; a process dust module for obtaining process dust indicators based on the main dust cluster area and the main injection gap; and a process monitoring module for obtaining production process monitoring results based on the process dust indicators. This invention improves the accuracy and stability of mine production process monitoring and management.

The invention discloses a method and a system for inverting an earthquake-induced soft and hard interbed anti-dip ancient landslide stratum structure, and relates to the technical field of earthquake-induced landslide simulation and prevention, and the method comprises the steps: reconstructing a terrain before instability to obtain a main slip direction geological profile before instability; a discrete element numerical simulation technology based on a damage theory is adopted to construct a plurality of soft and hard interbed reverse slope models with different soft and hard interbed thickness ratios, and then a dynamic failure process is simulated to obtain a simulated slope failure surface feature line; determining an actual terrain line based on unmanned aerial vehicle surveying and mapping data, and calculating a geometric matching degree according to the actual terrain line and the simulated slope failure surface feature line; and selecting the soft and hard interbed reverse inclination slope model with the maximum geometric matching degree, and marking the corresponding soft and hard interbed thickness ratio as a parameter of the soft and hard interbed reverse inclination ancient landslide stratum structure before earthquake induced instability. According to the method, the parameters of the soft and hard interbed anti-tipping ancient landslide stratum structure before earthquake-induced instability, which cannot be directly obtained through field investigation, can be calculated.

The invention belongs to the technical field of electric power distribution line inspection, and provides an insulator crack-light spot distinguishing method and system.In high-altitude unmanned aerial vehicle inspection, through closed track planning and three-gear exposure synchronous shooting in cooperation with a double-branch detection network, chain type geometric topology constraint and one-dimensional multi-flow graph convolution, the insulator crack-light spot distinguishing method and system are obtained; the method comprises the following steps: firstly, accessing a double-branch detection network by using highlight / dark fringe double-branch lightweight convolution to realize candidate crack detection; according to geometric matching, a candidate frame is aligned with a disc edge, three-exposure local parts are cut, and multi-dimensional features are extracted; after a highlight channel is attenuated by using a saturation mark, assembling features into a chain graph according to a physical sequence, and capturing cross-disk coherent dark fringes; and finally, the full connection layer outputs the crack probability, mirror reflection suppression and real crack suppression are realized, false alarm and missing alarm are effectively reduced, and an accurate basis is provided for a power failure maintenance decision.

The invention discloses a sky-ground collaborative observation method, and relates to the technical field of remote sensing. The method comprises the following steps: firstly, acquiring different coarse / high-resolution remote sensing data, ground measurement and Modis auxiliary data; and adjusting the angle information and the spectral information of the high-resolution remote sensing data to be consistent with those of the coarse-resolution remote sensing data based on the Modis auxiliary data. Extracting a coarse-resolution remote sensing data pixel space range as a template initial position, taking a high-resolution target variable data space resolution as a step length, moving the template, and simulating all geometric positions; and calculating the difference value between the pixel scale target variable aggregation value and the coarse resolution target variable original value during each movement, and determining the real geometric position of the pixel. And finally, in combination with actual ground observation data, calculating a difference value between the scale values of the pixels on the nominal position and the actual position, and quantifying the uncertainty in the sky-ground collaborative observation. According to the method, pixel-level geometric matching among the sky-ground multi-source data in a real sense is realized, the real response range of the reference value extracted from the satellite pixel is ensured, and the limitation of direct comparison in a traditional method is overcome.

Graphical element search technique selection, fuzzy logic selection for anchors and targets, and hierarchical graphical element identification for robotic process automation (RPA) are disclosed. The fuzzy logic selection of anchors and targets may be part of a larger, tiered, or hierarchical process for identifying graphical elements in the UI. When a selector for a UI element is not found with at least a confidence threshold, similar elements potentially corresponding to the selector for a UI element target may be searched based on fuzzy matching of the target and corresponding anchor(s). Geometric matching may also be employed between the target UI element and its respective anchor(s). The combination of fuzzy matching and geometric matching may allow for more flexible and accurate identification of the exact selector with which an RPA robot is attempting to interact.

The application discloses a kind of based on three-dimensional vision's chemical synthesis intelligent operation platform and control method thereof, belong to laboratory automation and intelligent manufacturing field.The platform includes three-dimensional vision acquisition module, point cloud physical property correction module, geometric matching and pose calculation module, motion control and safety constraint optimization module, self-supervision compensation learning module, drift detection and automatic calibration module, safety confidence decision module and mechanical arm execution module, each module is formed closed loop control system by data interface.The application is corrected by constructing refraction compensation model based on Fermat principle, optical deviation caused by transparent vessel and liquid level;Combined with fluid dynamics constraint optimization motion trajectory, prevent liquid splashing and cross contamination;Through self-supervision learning and automatic calibration mechanism, inhibit long-term running in coordinate drift, improve system accuracy and stability.The application can realize high-precision, high robustness and essential safety of automated chemical synthesis operation.

This invention belongs to the field of building construction and digital twin technology, and relates to an intelligent matching method and system based on artificial intelligence. This invention collects construction environment data in real time and identifies risk markers, calculates component deformation using a time-series prediction model, and corrects the virtual components generated by the BIM model for environmental compensation. It employs 3D point cloud registration technology for geometric matching analysis to determine hole alignment errors and interface gaps. Based on a dynamic decision tree model, it dynamically adjusts the installation sequence by comprehensively considering the degree of geometric mismatch, environmental risks, and construction logic. Furthermore, it incrementally updates the model based on actual installation deviations. This invention effectively solves the technical problem of component deformation and installation mismatch caused by environmental factors such as temperature and humidity during construction, achieving dynamic adaptation and precision control of the construction process, improving the matching success rate and construction efficiency of component assembly, and continuously optimizing the system's adaptability and reliability through continuous learning.

The present invention provides a method, device and electronic device for reconstructing a three-dimensional model from a two-dimensional design drawing of a cabinet. The method comprises: obtaining three views of the cabinet; analyzing the three views using a line detection deep neural network to obtain the position of the centerline of the side of the panel; correcting the position of the centerline of the side of the panel using primitive vector data, and obtaining the thickness information of the panel at the same time; and reconstructing the three-dimensional model of the cabinet through geometric matching based on the centerline of the panel. The present invention implements a method that supports fully automated import, and can automatically identify the corresponding three-view area based on the drawing file uploaded by the user, and automatically generate the corresponding three-dimensional model with accurate dimensions; the present invention can also read the spatial dimensions of the cabinet and the occlusion relationship between the panels from the three views, and infer the dimensions of unknown panels, so as to reconstruct a three-dimensional model of a cabinet with a more complex structure.

This invention provides a three-dimensional quantitative analysis and visualization method and system for aortic diameter expansion velocity based on longitudinal imaging data, applicable to the field of data processing. This application uses baseline and follow-up longitudinal aortic CTA images as the data source. Preprocessing includes spatial resampling and intensity normalization. The aortic lumen is extracted through medical image segmentation, and a voxel mask is generated. Then, the isosurface is reconstructed using the traveling cubes algorithm, and two sets of triangular surface mesh models are obtained through smoothing, cropping, repair, and remeshing. Using the baseline mesh as the source and the follow-up mesh as the target, topologically identical geometric matching is achieved through non-rigid registration. The centerline is extracted, and the point-by-point diameter is calculated. Combined with the follow-up time, three-dimensional quantitative data of the global diameter expansion velocity is obtained. Finally, multi-morphological visualization of expansion velocity and risk stratification is achieved through color mapping, 3D printing, and two-dimensional planar unfolding, constructing a complete three-dimensional quantitative analysis system for the aorta.

The application provides a 4D millimeter wave radar point cloud enhancement and target detection method based on Bayesian fusion, comprising the following steps: step one: projecting the 4D millimeter wave radar point cloud to an image plane and matching the image plane with an image instance segmentation result to obtain an image semantic category and an image segmentation confidence; step two: constructing a conditional probability density model based on the physical characteristics of the radar points; weighting and fusing the image segmentation confidence and the category probability density based on a Bayesian probability framework to obtain a fusion confidence; step three: using the fusion confidence and the semantic category as augmented features to splice the features of the 4D millimeter wave radar point cloud to generate a semantic enhanced point cloud; and using a fusion confidence guided sampling method to sample the semantic enhanced point cloud and inputting the semantic enhanced point cloud into a detection network for training and reasoning. The application solves the technical problems of one-way dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology.