167 results about "Single view" patented technology

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 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 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 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.

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.

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 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 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.

The invention relates to the technical field of computer graphics, and discloses a reflection prompt and self-correction-based large model three-dimensional vision generation method, which comprises a data acquisition stage, a data preprocessing stage, a coarse generation stage and a refinement stage, in the coarse generation stage, regularization is performed on a pre-trained 3D generation model through a symmetric guide diffusion mechanism, and the regularization is performed on the pre-trained 3D generation model; an initial neural radiation field with a consistent geometric structure is generated, in a refinement stage, the NeRF is refined by using a multi-loss optimization framework including reconstruction loss, diffusion prior loss and depth loss, and in the refinement stage, a DPF strategy is introduced, the strategy adaptively updates text prompts for SDS loss by using LLM, and the SDS loss is improved. And the signal is converted into a targeted correction signal. According to the method, a symmetric guide diffusion mechanism is introduced, symmetric geometric priori is applied in the reverse process of a 3D diffusion model, a geometric coherent and balanced basic shape is forcibly generated, and the ambiguity of a single view is effectively relieved.

The invention discloses a spatial perception multi-view anomaly detection method based on meta-view representation. The method comprises the following steps: obtaining multi-scale original features of a multi-view image of an input sample through a cascaded feature encoder; carrying out feature fusion on the multi-scale original features of the multi-view image, and obtaining meta-view representation through splicing, a full-connection network and a self-attention mechanism in sequence; performing a cross attention mechanism on the single-view image multi-scale features and the meta-view representation to obtain cross-view perceived single-view image multi-scale features, and splicing a plurality of cross-view perceived single-view image multi-scale features to obtain cross-view perceived multi-view image multi-scale features; according to the method, the cross-view perception multi-scale features of the multi-view image are compared with the original features of the multi-view image to perform anomaly detection, and the problem of view information loss of a traditional anomaly detection method is solved by extracting meta-view representation and performing cross-view perception, so that the accuracy of multi-view anomaly detection is improved.

The application discloses a single-view pose estimation method based on multi-modal input and attention mechanism, constructs a single-view pose estimation system including a prediction module and a pose regression module, combines multi-modal input and attention feature enhancement technology, learns various intermediate representation features of an object from a two-dimensional image, and then regresses the 6D pose of the object, and comprises the following steps: the prediction module adopts ResNet-18 as a backbone network, the module adds a channel attention mechanism to estimate various intermediate representations of the 6D pose of the object, including key points, edge vectors between the key points and symmetric corresponding relationships between pixel points; the pose regression module uses an EPnP algorithm and singular value decomposition to regress the 6D pose of the object from the intermediate representation result. The application provides a technology for accurately, conveniently and quickly estimating the 6D pose of an object from a single view.

The present application relates to power transmission channel monitoring technical field, specifically provide a kind of power transmission channel topographic risk assessment method and device, comprising: based on the single view complex image of ascending track and descending track SAR satellite covering power transmission channel area determines the gradient observed by satellite in ascending track and descending track direction;Extract the slope angle matrix and slope direction angle matrix of pixel point in digital elevation model covering power transmission channel area, and construct the projection matrix that will be mapped to radar line-of-sight direction under the motion of topographic coordinate system based on the slope angle matrix and slope direction angle matrix;Based on the gradient observed by satellite in ascending track and descending track direction and the projection matrix, determine the soil along slope sliding gradient and soil vertical settlement gradient covering the power transmission channel area;Based on the soil along slope sliding gradient and soil vertical settlement gradient covering the power transmission channel area, power transmission channel topographic risk assessment is carried out.The technical scheme provided by the present application can realize the fine classification of power transmission tower foundation risk.

This application discloses a dynamic form construction method, device, and medium, relating to the fields of software engineering and enterprise application development technology. The method includes: receiving a form request containing a view model identifier and request type based on a preset view parsing and execution engine; parsing a predefined view model according to the identifier to obtain its metadata, which defines the target business entity, view data object structure, field set, and filtering conditions; for data query requests, dynamically constructing and executing SQL statements based on the aforementioned metadata, obtaining raw data, performing aggregation and transformation, and generating structured view data that meets the front-end rendering requirements for return; for data submission requests, converting the data submitted from the front-end back into domain model data according to the same view model, and calling the business entity engine service to complete business logic verification and persistent storage, thereby achieving dynamic generation of the form view, decoupling of front-end and back-end logic, and ensuring business consistency.

The invention discloses an indoor scene reconstruction method and system based on a single view, a terminal and a storage medium. The method comprises the steps that an indoor single-view-angle RGB image of an indoor scene is acquired; generating a plurality of image features according to the indoor single-view RGB image, constructing a feature attention map and target object attributes according to the plurality of image features, and constructing an indoor preliminary model according to the feature attention map and the target object attributes; according to the plurality of image features and the target object attribute, predicting to obtain a background object attribute, and according to the background object attribute, optimizing the indoor preliminary model to obtain a final indoor model; and optimizing the final indoor model based on a differentiatable renderable pipeline, and generating complete shapes, appearances and postures of all objects in the indoor scene. According to the method, the image features are extracted from the single image, the final indoor model is deduced, the indoor scene is divided into foreground and background representation, and the geometric shape and appearance of the scene are restored robustly by optimizing the model.

The application discloses a kind of deep development tomographic SAR imaging method based on Taylor linearization and classification prior feedback, obtains multiple preprocessed two-dimensional single view complex image data;Constructing city area-oriented tomographic SAR imaging model;ADMM solving framework and augmented Lagrangian function are constructed, and original variable, auxiliary variable and multiplier variable are iteratively updated;After iterative optimization, high-precision city three-dimensional point cloud imaging result is output, and three-dimensional reconstruction of city scene is realized.The application breaks through the limitation that single criterion is difficult to remove high-amplitude false target, effectively suppresses false target caused by non-stationary noise using spatial continuity prior, significantly improves the accuracy of obtaining target height information;By jointly optimizing the Taylor approximation of physical model, the prior guidance of semantic classification and the continuity constraint of spatial geometry, not only the super-resolution ability is improved, but also the smoothness and accuracy of the ground object elevation are ensured while preserving the details of complex structure.

The application discloses a cross-view multi-target real-time trajectory tracking method, which combines a machine vision image acquisition system with a master control platform to realize distributed acquisition and centralized processing system architecture, realizes multi-target multi-view real-time image acquisition in a large scene through four sets or more than four sets of large field high-speed cameras, and realizes multi-target motion trajectory tracking. The application uses no less than four sets of acquisition systems to realize full-scene view coverage, solves the problems of poor target positioning accuracy and target position information loss caused by mutual shielding in the single view acquisition mode. Compared with a target positioning method based on a deep learning model, the multi-camera joint solution of the target position can realize higher positioning accuracy, better positioning robustness and higher frame rate.

The application provides a lightweight variable bit rate multi-view image compression method and system, and relates to the technical field of image compression. The method comprises: downsampling, feature extraction and feature scaling of a single-view image to obtain a latent representation corresponding to a target bit rate; quantization and lossless entropy coding of the latent representation to obtain a final compressed bit stream; subsequent lossless entropy decoding and inverse scaling to restore the latent representation; feature fusion and upsampling of the restored latent representations of different views to generate a reconstructed compressed image. The model comprises a main encoder, a feature scaling module, a quantization module, an autoregressive entropy model, an arithmetic encoder, an arithmetic decoder, a feature inverse scaling module and a decoder. The application can efficiently compress image data, reduce computational complexity and storage space occupation while preserving image details and quality, thereby providing faster speed and lower bandwidth requirement for image transmission and storage.

This invention discloses a pallet rental data management method, system, intelligent device, and storage medium. The method includes: obtaining the user identifier of the current user and the database identifier of the currently accessed database; obtaining a customer number list string based on the user identifier and database identifier; saving the customer number list string to a session variable; establishing a custom function to obtain the customer number list string; splitting the customer number list string into a row-based list view; establishing a data view for the relevant data tables in the currently accessed database; associating the list view with the data view to obtain the associated view; setting the relevant data tables to write-only; obtaining the query information input by the current user; retrieving result data from the associated view based on the query information; and providing the result data back to the current user. This invention ensures effective isolation of data in the shared pallet rental system and guarantees the confidentiality of user data.

The invention discloses a three-dimensional head dynamic reconstruction method based on a single view, and belongs to the field of computer vision. Constructing a multi-view face image data set; taking the left view, the front view and the right view of the same identity as input, and obtaining three-dimensional parameter representation of an initial static head through a pre-trained face parameter regression network; entering a dynamic reconstruction stage of single view input; projecting the Gaussian point cloud to an image plane of a single image through projection transformation, and performing feature extraction on the single image through a convolutional neural network; and carrying out pixel-by-pixel loss calculation on the rendered image and the real image, and optimizing the face parameter regression network. According to the method, a high-quality three-dimensional head model can be reconstructed through single-view input, a realistic head image at any view angle can be efficiently rendered, and the method has the advantages of being high in expression ability, high in rendering efficiency, capable of supporting dynamic updating and good in generalization.

The application provides a kind of marking method and system for 360 panoramic image, the method comprises: obtaining multiple single views covering full circumferential direction, splicing multiple single views to obtain panoramic image, calculating the coordinate conversion parameters between panoramic image and each single view, and writing corresponding coordinate conversion formula;Obtain 3D click coordinates when marking panoramic image, convert 3D click coordinates into map coordinates in panoramic image, map coordinates to each single view according to the coordinate conversion parameters;In the panoramic image, the current marking position and the next marking position are connected into a line in turn, until all the mapping marking lines in each single view are obtained.The application collects multiple single views in different directions and splices into panoramic image, only needs to mark once in panoramic image, can obtain the marking result of each single view according to the mapping relationship between panoramic image and each single view, greatly improves the marking efficiency and saves the computing power.

The invention discloses a fusion representation method for incomplete multi-view data, and belongs to the technical field of information technology service. The method comprises the following steps: aiming at the problem that a traditional multi-view learning method cannot be suitable for incomplete multi-view data learning when views are missing, constructing a multi-view representation matrix aiming at an incomplete multi-view data set; optimizing a single view representation matrix; a single-view reconstruction learning model is constructed, and complementation of a missing view representation matrix is realized; constructing a target function by using the fusion representation model; the fusion representation model is trained, an optimal fusion representation matrix is obtained through model optimization, and the fusion representation matrix can be directly used as input of downstream tasks. According to the method, the key technology of fusion expression of multi-source heterogeneous multi-view data is broken through, the purposes of making full use of multiple views and reducing influences of incomplete views are achieved, and then downstream tasks in a network can be supported.

The invention relates to the technical field of multi-source information processing, in particular to a multi-source information decision fusion method and system for a multi-granularity multi-view rough set, and the method comprises the following steps: obtaining multi-source heterogeneous data of an object to be decided, constructing a multi-source decision information table through cleaning and standardization processing, and dividing the information table into a plurality of view subsets; and constructing a multi-granularity hierarchical structure based on the view subset, calculating rough set approximation precision and attribute dependency under different granularity hierarchies, and generating a local decision rule set. And performing weighted aggregation on the local decision rule set by using a multi-source information fusion algorithm, and constructing decision evaluation parameters based on consistency measurement. According to the method, the problems of one-sided information under a single view angle and detail loss under a single granularity are effectively solved through the calculation of the multi-view and multi-granularity collaborative mechanism reverse driving decision cost, and the accuracy and robustness of decision analysis under a complex and uncertain environment are remarkably improved based on the feedback adjustment of the dynamic weight; and deep mining and efficient fusion of multi-source information are realized.

The utility model discloses a rat and mouse gait fine behavior monitor which comprises a machine body, a runway for a small animal to pass through is arranged in the middle of the upper end face of a base body of the machine body, the runway is defined by a bottom plate and two transparent partition plates, the width between the partition plates is adjustable, and lead screw assemblies are installed above the two ends of the partition plates respectively. An image acquisition device and a processor connected with the image acquisition device are arranged below the bottom plate, side-view imaging mirrors with adjustable angles are arranged on the two sides of the runway, a turnover cover body provided with a light source assembly is arranged on the seat body, and an inlet and an outlet corresponding to the runway are formed in the two side surfaces of the cover body. By adopting a runway bottom imaging and side-view imaging mirror structure on two sides, the three-dimensional imaging system can synchronously capture right, left and bottom views of a moving object, realizes a three-dimensional imaging effect, can accurately reconstruct a three-dimensional motion track, eliminates a blind area caused by a single view angle, and ensures that the obtained motion track information is complete and accurate.

The invention provides an occluded pedestrian re-identification method and system based on multi-view hierarchical semantic fusion, belongs to the technical field of computer vision, and aims to solve the problems of insufficient cross-view discrimination capability, fuzzy semantic hierarchy and insufficient multi-view fusion caused by single-view feature modeling in the prior art. Through a multi-view hierarchical semantic fusion mechanism, structural and semantic consistency information of pedestrians at different views can be fully mined, and high-robustness and high-discrimination identity feature expression is realized in combination with an adaptive feature fusion strategy; according to the scheme, stable recognition performance is still kept in complex environments such as shielding, illumination change and posture change, and the method can be conveniently embedded into an existing monitoring analysis system through modular design, so that real-time recognition and continuous tracking of pedestrian identities are achieved.

The application discloses a Direct3D 12 to rendering metadata mapping method based on kernel level nested analysis, establishes a structure body and a global index of D3D metadata in a VKD3D compilation stage, creates D3D metadata UBO when starting, intercepts resource binding, dynamic state configuration, multi-view setting and drawing calling and the like operations of VKD3D when executing, captures related parameters and generates complete D3D metadata in combination with kernel analysis, groups metadata according to a mapping dictionary between system values dependent on D3D12 colorizers and metadata demand types, writes the grouped metadata into the UBO of the corresponding metadata group according to a single view or multi-view scene, combines the metadata into a drawing group, submits the command buffer to the GPU to complete rendering after batch recording of the drawing command, and realizes compatible running of the D3D12 application in the case that the GPU does not support the ShaderDrawParameters feature.

The invention provides a departure behavior detection method and device, equipment and a storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining video streams of at least two cameras disposed in a to-be-detected space, and obtaining station region images in the video streams of the at least two cameras; inputting the station area images in the video streams of the at least two cameras into a detection model for human body detection; and determining a departure illegal behavior based on a human body detection result. By adopting the multi-view video stream of the at least two cameras, the problems of missing detection and misjudgment caused by shielding, illumination and other factors under a single view angle are relieved, interference of background noise and human body characteristics of adjacent areas is avoided by extracting station area images and performing targeted detection, and the detection precision is improved.