294 results about "Single view" patented technology

The invention relates to the technical field of natural language processing and information retrieval, and discloses a deep dense document recall method based on multi-view vector fusion, comprising the following steps: S1, preprocessing user query and candidate documents to generate a standardized text; s2, respectively constructing multi-view semantic vector representation of the query and the document, wherein multiple views at least comprise a keyword view, a semantic extension view and an intention view; s3, calculating a similarity score between the query and the document for each semantic view, and fusing the scores of all the views through a dynamic weight; and S4, sorting the documents according to the fusion score, and returning a front Top-K result. By constructing multi-dimensional semantic representation of keywords, semantic extension and an intention perspective, multiple semantic information such as term accurate matching, context association and task target consistency is effectively fused. The dynamic weight distribution mechanism adaptively adjusts the contribution degree of each view angle according to the query content, and overcomes the defect of insufficient semantic coverage of a single view angle.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention provides a single-view image three-dimensional modeling method and system based on comparative learning, and the method comprises the steps: starting from a single input image, generating a multi-view candidate image through a pre-training two-dimensional diffusion model in combination with score distillation sampling; performing positive and negative sample division on the candidate image pairs based on texture fidelity and structure coherence; constructing a perceptual contrast learning mechanism, calculating perceptual loss between the images, and optimizing texture consistency in a three-dimensional content generation process; proposing a number-aware triple loss function, and dynamically adjusting the contribution ratio of positive and negative samples in training; then, a super-resolution reconstruction module is embedded to perform detail enhancement on the image, and the quality of a contrast gradient signal is improved; and finally, performing joint training on the perception comparison loss and a neural radiation field or volume rendering module to generate a high-quality three-dimensional grid or volume rendering result. The problems that in an existing diffusion generation method, multi-view textures are inconsistent, structure reconstruction is not coherent, and the semantic distinguishing capacity is insufficient are effectively solved.

The invention discloses a single-view three-dimensional dressed human body reconstruction method based on pixel and voxel feature fusion. The method comprises the steps of data set preprocessing, data set dividing, three-dimensional dressed human body reconstruction network construction, three-dimensional dressed human body reconstruction network training, three-dimensional dressed human body reconstruction network testing and reconstruction result displaying. In the pixel feature extraction network, the coordinate attention and the stacking hourglass network are combined, so that the pixel feature extraction precision is improved; in the voxel feature extraction network, a three-dimensional channel and space attention are introduced into the voxelization human body parameterization model network, and a multi-scale residual feature extraction module is combined to extract voxel features. And splicing the extracted pixels and voxel feature vectors, inputting the spliced pixels and voxel feature vectors into a multi-layer perceptron, and reconstructing a three-dimensional dressed human body model through Marking Cubes, thereby solving the technical problem of insufficient precision of the existing single-view three-dimensional dressed human body reconstruction method, and being applicable to the technical fields of artificial intelligence and computer vision.

Methods, devices, and processor-readable media for method for performing a 3D reconstruction from a single view image, comprising progressively denoising a randomly initialized set of 3D-gaussian representations with continuous guidance from an input image.

The invention relates to a robot environment perception method based on single-view three-dimensional scene generation, and the method comprises the steps: collecting a two-dimensional image containing target environment information through employing a monocular camera, generating multi-view information through combining depth estimation, normal prediction and a two-stage semantic guidance diffusion model, and constructing a high-quality three-dimensional scene. And reconstructing three-dimensional point cloud data through the neural radiation field, and performing texture rendering optimization on the point cloud data. A Point Net + + network is used for carrying out semantic analysis on point clouds, a multi-frame time sequence point cloud registration and Kalman filtering tracking method is introduced, modeling is carried out on a dynamic target, and a dynamic semantic map with a motion state is constructed. And finally, structured output environment information is used for robot navigation, path planning and task execution. The problems of high cost, high complexity and insufficient real-time performance and robustness of a three-dimensional scene generation technology in the field of robot environment perception are solved, and the method is high in structuring degree, standard and unified in output format and high in universality and engineering adaptation capacity.

The invention provides a three-dimensional model retrieval method based on multi-view aggregation, and the method comprises the steps: 1, designing a three-dimensional model retrieval network based on views, and enabling the three-dimensional model retrieval network to extract the features of a three-dimensional model from the view representation of the three-dimensional model for three-dimensional model retrieval; the three-dimensional model retrieval network MVAT comprises a single-view feature extraction network and a multi-view feature aggregation network, the single-view feature extraction network is responsible for independently extracting local features of a three-dimensional model from each view, and the multi-view feature aggregation network is responsible for generating global feature representation through information fusion of multiple views; 2, weight freezing and contrast learning technologies are introduced, and a three-stage training method is adopted to train the three-dimensional model retrieval network; and 3, performing performance evaluation on the trained three-dimensional model retrieval network on a test set. The three-dimensional model retrieval method has the advantages of being high in feature expression ability, efficient in training method, high in precision and good in generalization.

The invention discloses a cultural relic handicraft three-dimensional reconstruction method based on multiple views and deep learning, and relates to the technical field of cultural relic handicraft three-dimensional reconstruction methods, and the method comprises the steps: carrying out the image collection of cultural relic handicrafts through employing a multi-view shooting method, and obtaining a structured image data set; performing feature matching and camera parameter estimation on the obtained multi-view image set by adopting a sparse reconstruction method to obtain camera internal parameters, external parameters and sparse point cloud data; performing fine processing on the obtained sparse point cloud data by adopting a dense reconstruction method to obtain a 3D dense point cloud model with a texture map; analyzing the obtained 3D dense point cloud model by adopting a depth map generation method to obtain a depth map and a rendering image corresponding to each image; image acquisition is performed on the cultural relic handicraft by adopting a multi-view shooting method, so that information such as geometric shapes and texture details of the cultural relic handicraft is comprehensively captured, and the problem of information loss caused by a single view angle is effectively solved.

The invention discloses a single-view human body three-dimensional reconstruction method based on Gaussian surface elements, and the method comprises the steps: collecting image data from a plurality of human body data sets, carrying out the preprocessing through employing a targeted preprocessing strategy according to the data characteristics corresponding to each image data, and obtaining a processed human body data set; constructing a human body 3D Gaussian attribute prediction model, wherein the human body 3D Gaussian attribute prediction model comprises a hierarchical feature extraction module, a feature fusion network and a 3D Gaussian function decoder; training the human body 3D Gaussian attribute prediction model based on the processed human body data set to obtain a trained human body 3D Gaussian attribute prediction model; and performing three-dimensional reconstruction on an input human body picture based on the trained human body 3D Gaussian attribute prediction model, and outputting a reconstructed human body three-dimensional image. According to the method, the manufacturing cost of the virtual digital human is greatly reduced, and the efficiency and accuracy of constructing the dynamic human body model are improved.

The invention provides a multi-view structure learning method based on multi-expert cooperation, and relates to the technical field of graph neural networks and multi-view learning, and the method comprises the steps: reconstructing initial multi-view data, and obtaining multi-view structure data; respectively training the single-view expert model and the shared expert model by utilizing a first loss function and a second loss function based on the multi-view structure data to obtain a trained single-view expert model and a trained shared expert model; constructing a collaborative decision model by using the trained single-view expert model and the trained shared expert model; training the collaborative decision model by using a third loss function to obtain a trained collaborative decision model; and analyzing the graph data by using the trained collaborative decision model to obtain a joint decision result, and completing learning of the multi-view structure. According to the method, the problems of large structural noise interference, isolated expert model information and insufficient node classification accuracy when an existing graph neural network processes multi-relation graph data are solved.

The invention discloses a single-view unknown object 6D pose estimation method based on segmentation and new view angle synthesis, and relates to the field of object pose estimation. The method comprises the following steps: designing a segmentation module and a new view angle synthesis module of a pose estimation network, and obtaining a segmentation result of a target object by using the segmentation module; generating a target object multi-view-angle embedding template by using a new view angle synthesis module; matching the embedding representation zquery'of the segmentation result with a multi-view embedding template of the target object to find out the embedding representation most similar to zquery ', and determining a preliminary rotation attitude estimation result R of the target object; and estimating the relative translation vector t of the target object, and combining the R to obtain the relative pose (R, t) of the target object. According to the method, robust 6D pose estimation of the unknown object in a complex visual scene can be realized only by taking a single reference image as input, and the pose estimation precision and robustness of the unknown object in the complex scene are improved while the real-time requirement is met.

The invention discloses a single view reconstruction and rendering method, which belongs to the field of view reconstruction and rendering, and comprises the following steps of: firstly, generating multi-view feature representation with strong geometric consistency from a single input image by introducing an image diffusion module of a cross attention mechanism; a point cloud reconstruction module with self-attention and cross-attention is utilized, and multi-view information is fused to reconstruct an accurate three-dimensional point cloud; and finally, constructing differentiable three-dimensional Gaussian representation based on the point cloud, rendering the differentiable three-dimensional Gaussian representation, and outputting a new view angle image, a normal map and a depth map. According to the system, a staged training strategy is adopted, and a composite loss function including multi-scale bidirectional consistency smooth loss and feature consistency loss is innovatively used for optimization. According to the method, the problems of low geometric accuracy, multi-view inconsistency, detail missing and the like in single-view reconstruction are effectively solved, and the method can be widely applied to the fields of virtual reality, digital twinning, cultural heritage digitization and the like.

The invention belongs to the technical field of image processing and deep-sea mining, and discloses a deep-sea polymetallic nodule single-view three-dimensional reconstruction method and system. Aiming at the problem that the real three-dimensional data of the underwater polymetallic nodule is difficult to acquire, a multi-view generation model is adopted as 2D prior, 3D Gaussian is optimized through fractional distillation sampling loss, any three-dimensional data training is not used, the problem of three-dimensional data acquisition is solved, and three-dimensional reconstruction of the polymetallic nodule is realized. In addition, the invention provides a method only through image fine tuning, function decoupling of a noise prediction module (Unet) and a condition control network (multi-view model) is realized based on DreamBooth, and multi-view generation stability can still be maintained through pre-training view angle parameters under empty condition input; and the problem of parameter acquisition of the underwater camera is avoided through a conditional blanking mechanism, so that the model can be finely adjusted only by depending on a single-view image, and the data acquisition cost is reduced.

The invention relates to the technical field of city data representation. The invention discloses an urban space general representation learning method and device based on multi-modal spatio-temporal data fusion, a terminal and a storage medium, which can improve the applicability of urban general representation and enable the urban general representation to be applicable to diversified urban analysis tasks. The method comprises the following steps: acquiring multiple modal spatio-temporal data of each space unit in a target city, and setting a corresponding view for each modal spatio-temporal data; on the basis of each modal spatio-temporal data of each space unit, generating a representation of each space unit in a single view under a view corresponding to each modal spatio-temporal data; generating a multi-view fusion representation corresponding to each space unit based on the representation of each space unit in a single view under the views corresponding to all modal spatio-temporal data, and forming a multi-view fusion representation matrix by the multi-view fusion representations corresponding to all the space units; and performing global aggregation processing on the multi-view fusion representation matrix to obtain urban general representation.

Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

The invention provides a cross-view-angle sight line estimation method based on feature decoupling and an attention mechanism, double-view-angle face images are collected through double cameras, a view angle blind area under a single view angle is effectively eliminated, and the ambiguity problem caused by insufficient single-view-angle information is relieved; face features are decoupled into personal appearance features, head posture features and sight line related features, and accurate separation of different types of features is achieved; the adaptive capacity of the model in diversified scenes is enhanced through a feature decoupling strategy, and it is ensured that the model still keeps high-performance under the conditions of different individuals and large-amplitude head posture changes; then, adaptive weighting processing is carried out on the three types of features obtained through decoupling by applying a cross attention mechanism, and supplement and enhancement of sight line information are realized through fusion of double-view-angle sight line features; meanwhile, the influence of personal factors and head movement is compensated by combining the processed appearance features and head posture features, so that the precision and robustness of sight line estimation are remarkably improved.

The invention discloses a multi-article binding recommendation method based on graph contrast learning, and relates to the technical field of e-commerce application. According to the method, through heterogeneous binding relation modeling, a user-article-binding ternary interaction graph is constructed, direct preference of a user to an article and indirect association of binding driving are jointly learned, and the problem of relation splitting caused by multi-graph splitting in AMCBR and MGCRL is solved; according to the method, light-weight projection head optimization is carried out, double-layer linear projection is adopted to replace a traditional multi-layer perceptron (MLP), the over-smoothing risk is reduced while the contrast characterization distinction degree is enhanced, and the redundancy problem caused by fusion or knowledge modules in MultiCBR and KEMB-Rec is solved; the method introduces a multi-objective loss function, combines main task recommendation loss, cross-view comparison loss and binding comparison loss, and collaboratively optimizes local and global embedding representation, is more comprehensive than EBRec single view loss, and effectively alleviates sparsity and over-smoothing problems.

The invention relates to the technical field of computer vision, in particular to a gymnastic training real-time feedback and error correction system based on image recognition and tracking, and the system comprises a data collection module which is used for obtaining a three-dimensional space coordinate system of a training field, and synchronously collecting the training actions of gymnastic athletes through a plurality of cameras, so as to obtain a multi-view real-time training video stream; and the image processing module is used for carrying out frame image decomposition on the multi-view real-time training video stream to obtain continuous multi-view action frame images. According to the invention, through the data acquisition module and the skeleton generation module, real-time three-dimensional attitude skeleton data under a three-dimensional space coordinate system of a training field can be obtained, so that the defects that the traditional video playback lacks space depth information and cannot truly restore the three-dimensional attitude are fundamentally overcome; meanwhile, the problems that the visual angle is single and motion details cannot be captured in all directions during visual observation of a coach are solved, and a complete three-dimensional space data basis is provided for subsequent accurate analysis.

The invention discloses a construction period landslide disaster advanced prediction and rapid networking monitoring method, and the method comprises the steps: collecting synthetic aperture radar data, digital elevation model data and satellite precision orbit data of a monitoring region, carrying out the data preprocessing, and obtaining a single-view complex data image set; constructing an interference image pair connection graph, performing interference processing, and generating a differential interference graph and a coherence coefficient graph; performing interferometric phase unwrapping processing on the differential interferogram and the coherence coefficient graph according to a minimum cost flow algorithm to obtain earth surface deformation information; ground control points are selected, and the surface accumulated deformation quantity and the surface deformation rate are obtained; interpreting and delineating a potential deformation area, establishing an interpretation mark, selecting a typical deformation area, and extracting a time sequence deformation data set; and analyzing the time sequence deformation data set according to the neural network model to obtain a deformation prediction result and a disaster risk assessment result. According to the invention, the monitoring accuracy and prediction capability of landslide disasters can be improved.

The invention relates to the technical field of machine vision control, in particular to an automatic setting control method and system based on real-time image acquisition, and the method comprises the following steps: calling an image sequence, analyzing light spots of a structured light array, merging a coordinate connection contour, fusing multi-view gray difference, constructing a reference path, and analyzing a load change trend. And adjusting the track instruction and the rollback condition, and generating angle rollback reference data. According to the method, through precise merging analysis of contour pixel coordinates based on the structured light array and combination of multi-view gray region difference identification, the contour form correction precision and the reliability of workpiece surface path construction are remarkably improved, and according to correlation analysis of continuous load data and trajectory change, trajectory offset and rollback angles are adaptively adjusted, so that the accuracy of the contour form correction is improved. The phenomena of contour recognition errors and load change neglect caused by a single view angle are effectively overcome, the stable control and automatic deviation correction effects of a workpiece track are achieved, and the stable operation capacity of a control system is improved.

An embodiment of the present application provides a 3D endoscope, including: a mirror body, including a lens and a snake bone assembly, the lens being connected to the distal end of the snake bone assembly; a plurality of traction ropes, connected to the distal end of the snake bone assembly to enable the snake bone assembly to bend in multiple directions; a wire winding assembly, arranged at the proximal end of the mirror body, and the plurality of traction ropes are wound around the wire winding assembly; an elastic tube, the plurality of traction ropes respectively pass through the elastic tube, the elastic tube is connected to the proximal end of the snake bone assembly and extends towards the proximal end of the mirror body, and the actual length of the elastic tube is greater than the total straight-line length between the proximal end of the elastic tube on the snake bone assembly and the distal end of the elastic tube on the mirror body. The embodiment of the present application solves the technical problem of the single viewing angle of the 3D endoscope.

The invention provides a grabbing control method and device of a robot, the robot, a storage medium and a program product. A first collecting assembly is controlled to collect first sensing data in the current environment where the robot is located, and a mechanical arm is controlled to drive a second collecting assembly to collect second sensing data in the current environment; and controlling the mechanical arm to grab the target object based on the first sensing data and the second sensing data. Thus, the target object can be grabbed according to the first sensing data and the second sensing data obtained by the first collection assembly and the second collection assembly which are different in position, the target object can be grabbed according to data of different view angles, the possibility of misrecognition caused by limited obtained information under a single view angle is reduced, and the recognition accuracy is improved. And the accuracy and the flexibility of the grabbing control method are improved.

The invention relates to the technical field of image generation, in particular to a medical beauty 3D model construction method and system based on an image large model, and the method comprises the following steps: obtaining rasterized image data based on a 3D full-head point cloud model through space coordinate projection and pixel mapping, judging connectivity and a missing region, screening region features, and generating texture guidance parameters; and screening textures according to partition features, determining a multi-region fitting index, and outputting a continuous boundary fusion region. According to the invention, through carrying out multi-angle spatial reconstruction and fine pixel mapping on the three-dimensional point cloud data, texture coverage is not limited to a single view angle, spatial feature partitioning and target attribute identification, so that accurate supplementation of the texture of the missing region is realized; the locally generated texture content and the original input region are highly fused in the aspects of color structure and detail representation, and region mutation is effectively eliminated through spatial adaptation and boundary adaptive processing, smooth connection of multi-part texture splicing presentation, color transition and gradient fusion.

The invention provides a multi-view point cloud data quality enhancement method for aviation assembly gap measurement, and the method comprises the steps: enabling a line laser sensor to be fixed to a multi-view collection device, and collecting the point cloud of a to-be-measured gap from a plurality of views; converting the plurality of slit point clouds collected under the plurality of visual angles into the same coordinate system, and preprocessing the data; curvature features of the gap point cloud are calculated, and edge points, namely critical points, of the skin surfaces on the two sides of the gap are extracted; performing linear fitting on the critical point by adopting a least square method, and determining a gap direction; projecting the three preprocessed slit point clouds to a plane perpendicular to the slit direction along the slit direction; and merging the three slit point clouds, and carrying out denoising and down-sampling processing to obtain complete slit point cloud data at a certain point to be measured. According to the method, the problems of incomplete data acquisition under a single view angle and large accumulative error of traditional multi-view angle data acquisition are solved, the measurement quality of the gap data is optimized, and the details and characteristics of the gap data are enhanced.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for automatic switching between visual responses, audio responses and textual responses in an omni-channel single view experience. Particularly, the system and method may empower a user to choose between any mode of interaction, the modes being provision of a visual interaction, audio interaction or a textual based interaction and a combination thereof based on a machine learning architecture and also provide seamless human agent handover. Thus, the system and method of the present disclosure may be beneficial for both entities and users.

The invention relates to the technical field of examination cheating prevention, and discloses an online examination cheating prevention real-time early warning system based on behavior recognition, and the system comprises a real-time monitoring module which employs multi-mode biological feature recognition, carries out the confirmation of the identity and registration information of an examinee through face recognition before an examination, and carries out the real-time monitoring of an examination behavior; the test module is used for identifying examinee information and providing examination questions and an answer interface; the AI algorithm recognition module monitors behavior characteristics of the examinee in the examination process, recognizes abnormal behaviors by analyzing the stability of the behavior characteristics and judges whether the behaviors are cheating behaviors, and a voiceprint recognition module is arranged in the AI algorithm recognition module to monitor environmental noise and record voice. According to the invention, through synchronous acquisition of three paths of videos, a monitoring blind area is further eliminated, multiple paths of videos complement each other, misjudgment of a single view angle is reduced, behaviors of examinees can be automatically identified and analyzed through an AI algorithm, risk prompting and early warning are carried out, and the monitoring efficiency is improved.

The invention provides a stacked material volume identification and generation method fusing single view 3D reconstruction and BIM calibration, and relates to the technical field of stacked material volume generation, and the method employs a low-cost monocular camera to collect images, and generates a three-dimensional model of stacked materials by means of an advanced deep learning model and a three-dimensional reconstruction algorithm. Through scale calibration with a virtual camera in the BIM model, the problem of scale uncertainty which is difficult to overcome in traditional single-view reconstruction is solved, so that the real volume of the stacked material can be accurately calculated. In addition, through automatic target detection, three-dimensional coordinate calculation and volume calibration processes, the measurement efficiency is greatly improved, and manual intervention is reduced. The method is not only suitable for volume measurement of static stacked materials, but also can be expanded to real-time monitoring of dynamic construction scenes, and provides powerful technical support for construction management.

The invention relates to the field of low-carbon life, and discloses a multi-dimensional simulation evaluation method for a low-carbon life policy effect, and the method comprises the following steps: S1, building a macroscopic intervention conduction model and a microcosmic heterogeneous agent model; s2, based on the microscopic heterogeneous agent model, driving the agent to make a low-carbon behavior decision; s3, polymerizing low-carbon behaviors of the intelligent agent at the current simulation time step, and taking the low-carbon behaviors as input of the macroscopic intervention conduction model; s4, dynamically adjusting macroscopic intervention variables based on the influence factors, and judging a low-carbon behavior decision according to the microscopic heterogeneous agent model; and S5, iteratively executing a simulation period, and performing multi-dimensional evaluation and analysis on a simulation result generated in the simulation process. Through a double-layer simulation model technology, in combination with a macroscopic intervention conduction model and a microcosmic heterogeneous agent model, multi-dimensional evaluation of a low-carbon policy effect is realized, and the defect that the policy effect cannot be comprehensively grasped under a single view angle is overcome.

The invention relates to the technical field of general image data processing or generation, in particular to a multi-view indirect immunofluorescence medical image classification interpretation method and system, and the method comprises the steps: mapping an ethanol view, a formaldehyde view and an HEp-2 view to a feature space, and converting into a feature sequence; performing position coding on the feature sequence to obtain position information; based on the feature sequence with the position information, modal identification information is added, and an identification feature sequence is determined; based on the identification feature sequence, executing multi-modal information fusion, and determining a multi-modal feature sequence; based on the multi-modal feature sequence, combining an attention mechanism to determine a long-distance dependency relationship and correlation between the data; and based on the attention mechanism model, outputting the probability of each category to execute classification interpretation. According to the method, the type of the anti-neutrophil cytoplasm antibody fluorescence model can be adaptively and comprehensively judged in combination with the characteristics of fluorescence brightness, forms and the like of different views, and interference existing in a single view is avoided.

The invention provides a multi-view space transcriptomics clustering method based on sharing-specific information mining. The technical problems that in an existing space domain recognition method, robustness of a single view is insufficient, multi-view information fusion is insufficient, and description of data statistical characteristics is not accurate are solved. According to the technical scheme, the method comprises the following steps: S1, acquiring and preprocessing original data of a spatial transcriptome; s2, constructing a sharing-specific decomposition coding network; s3, using a ZINB expression decoder, a structure decoder and a Student's t distribution clustering module to construct a multi-task loss function; and S4, outputting a spatial domain division result based on the trained network. According to the method, the spatial domain recognition precision and robustness are remarkably superior to those of an existing method, the data statistical characteristics and biological significance can be accurately described, stable support is provided for tissue function analysis and tumor microenvironment research, and the analysis quality and application value of spatial transcriptomics data are effectively improved.