2429 results about "Reconstruction method" patented technology

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention provides a multi-view three-dimensional point cloud reconstruction method and device based on DPE-SE depth estimation, and relates to the technical field of computer vision and three-dimensional reconstruction. The method comprises the following steps: acquiring multi-view image data; preprocessing the image; inputting the preprocessed image into a DPE-SE-based depth estimation model, carrying out key constraint on an edge region through a semantic edge guiding mechanism, carrying out adaptive propagation updating on a weak texture region, realizing accurate depth estimation, and generating a multi-view depth result; then geometric consistency check and multi-scale depth fusion are performed on a multi-view depth result, and a dense depth map is constructed; and finally, performing three-dimensional back projection reconstruction and point cloud optimization processing, and outputting high-quality point cloud data containing three-dimensional coordinates and confidence information. According to the method, the problems of edge mismatching and depth voids are remarkably improved in complex illumination, weak texture and shielding environments, the continuity and structural integrity of the point cloud are improved, and technical support is provided for unmanned aerial vehicle surveying and mapping, building detection and digital twin modeling.

The invention discloses a dynamic scene three-dimensional reconstruction method and device based on hydrogen energy unmanned aerial vehicle survey, and the method comprises the steps: obtaining dense time sequence multi-view image data of a target region through a hydrogen energy unmanned aerial vehicle platform, and carrying out the preprocessing of radiation correction and geometric correction; carrying out optical flow analysis and deformation rate clustering on the preprocessed image, identifying a pseudo-static anchor point and constructing a dynamic reference field; introducing a dynamic reference field as a soft constraint in a binding adjustment process, and optimizing a camera pose to generate a three-dimensional point cloud with consistent time and space; and finally, mapping the point cloud to a space-time voxel grid, constructing a surface evolution model by using a graph neural network or an anisotropic diffusion algorithm, and calculating a surface deformation vector to realize continuous and high-precision three-dimensional reconstruction of the disaster scene surface deformation process.

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

The invention discloses a river flow missing data reconstruction method, which comprises the following steps of S1, constructing a river network topological structure, and quantifying hydraulic correlation; s2, spatio-temporal feature fusion and multi-source information extraction; s3, performing multi-task cooperative flow reconstruction and confidence coefficient prediction; s4, dynamic weighting and result correction of meteorological factors; and S5, performing anomaly detection and model dynamic updating. According to the method, a river flow missing data reconstruction method is set, the steps are mutually fused and used, and topology-aware space-time diagram convolution is performed: a river network topology structure is encoded into a weighted adjacency matrix for the first time, space correlation features are extracted through a diagram convolution network, and the problem that a traditional method ignores hydraulic connection is solved; a multi-task collaborative learning mechanism: synchronously outputting a flow reconstruction value and confidence, combining topological smooth constraints, and realizing reconstruction reliability quantification while ensuring precision; and meteorological dynamic weighted correction: dynamically adjusting the node weight based on the real-time rainfall intensity, and accurately adapting to the nonlinear response of the water flow in the heavy rainfall period.

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

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention relates to the technical field of virtual reality, in particular to an indoor structure reconstruction method based on a panoramic image scene understanding algorithm, and the method comprises the steps: S11, obtaining a plurality of equidistant columnar projection panoramic images of a current indoor scene through a panoramic camera or a panoramic image splicing algorithm; s12, performing semantic segmentation on the collected panoramic image by using SAM, deducing indoor ceiling, floor and wall areas of the panoramic image on the basis of a semantic segmentation result, and generating a multi-channel semantic graph; and S13, inputting the acquired RGB panoramic images and the multi-channel semantic map into a pre-trained panoramic image depth estimation model to obtain a depth map corresponding to each panoramic image. The method is used for automatically generating an indoor structure model, the scheme comprises the core steps of semantic segmentation and layout reasoning, depth estimation and point cloud construction, structure optimization and indoor model reconstruction and the like, and a complete indoor panoramic image scene understanding scheme is formed.

Embodiments of the present disclosure provide a three-dimensional scene reconstruction method and apparatus, a device, a medium, and a program product. The three-dimensional scene reconstruction method comprises: acquiring a scene image collected for a three-dimensional scene; on the basis of the scene image, determining sparse point cloud data and camera parameter information corresponding to the scene image; performing model initialization on the basis of the sparse point cloud data to obtain a current three-dimensional data model; performing rendering on the basis of the camera parameter information and the current three-dimensional data model to obtain a current color map, a current depth map and a current normal map from the perspective of the scene image, and determining a pseudo normal map from the perspective of the scene image on the basis of the current depth map; and on the basis of the current color map, the current normal map, the pseudo normal map, and an actual color map, training the current three-dimensional data model to obtain a trained target three-dimensional data model. By means of the technical solution provided by the embodiments of the present disclosure, higher-quality automatic reconstruction of three-dimensional scenes can be achieved, reducing reconstruction costs, and improving the level of detail and rendering quality of scene models.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention discloses an efficient image super-resolution reconstruction method and system based on degradation region guidance, and the method comprises the following steps: S1, carrying out the region-level degradation type recognition and severity quantification of an input low-resolution image, and generating a global degradation distribution map with spatial consistency; s2, according to the global degradation distribution map and in combination with semantic-texture collaborative features, repairing a region which is judged to be seriously degraded by adopting a high-capacity branch, and repairing a region which is judged to be slightly degraded by adopting a light-weight branch; s3, fusing the output of the high-capacity branch, the output of the lightweight branch and the global detail enhanced image to generate a final high-resolution image; wherein the global detail enhanced image is obtained by enhancing the semantic-texture collaborative features.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a lightweight three-dimensional reconstruction method and system based on a three-dimensional Gaussian model, and the method comprises the steps: constructing the three-dimensional Gaussian of a target scene based on a multi-view original image of the target scene through employing a 3DGS algorithm; identifying the minimum resolution of each Gaussian primitive in the three-dimensional Gaussian, setting a spherical region according to the minimum resolution, taking the intersection result of the spherical region and the Gaussian primitive as a region evaluation score, and trimming the redundancy region geometry in the three-dimensional Gaussian in combination with the opacity; and taking the maximum zoom scale of the Gaussian primitive as a spatial density proxy variable, and dynamically distributing spherical harmonic coefficient orders according to a local spatial density proxy variable so as to meet the rendering requirements of different three-dimensional Gaussian geometric regions. On the premise that the image synthesis quality is not lost, the model scale and the transmission overhead can be remarkably compressed, the resource constraint condition of the edge computing equipment can be effectively adapted, and the higher training speed and the better rendering effect are achieved.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

The invention discloses a physical field reconstruction method fusing a reduced-order model and a multi-fidelity model, and belongs to the technical field of data-driven physical field reconstruction. The method comprises the following steps of: firstly, carrying out nonlinear dimension reduction on high-dimensional simulation data by utilizing a deep auto-encoder, extracting a low-dimensional feature vector, training an encoder-decoder network, and establishing bidirectional mapping between high-dimensional data and low-dimensional data; then, a mapping model from the working condition parameters to the low-dimensional features is constructed and used for predicting feature vectors under the new working condition; the prediction features are then reconstructed by a decoder into preliminary physical field data as a low fidelity trend. And finally, on the basis of the trend, in combination with sparse high-fidelity measured data, correction is carried out through a multi-fidelity fusion model, and a high-precision reconstructed physical field is obtained. According to the method, the precision and credibility of physical field prediction are effectively improved by fusing multi-source data, and the method is suitable for the fields of structural health monitoring, digital twinning and the like.

The invention discloses a depth-guided three-dimensional Gaussian reconstruction method and system suitable for a sparse view angle image, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the wavelet transformation super-resolution processing of a sparse multi-view angle image; outputting depth prior through a pre-trained monocular depth model, and extracting multi-view image features; constructing cross-view depth candidates by adopting a planar scanning stereo method, and generating initial depth distribution through feature similarity calculation; a self-attention-cross attention structure and deformable sampling are adopted to realize coarse-to-fine depth matching optimization; using an improved UNet network to fuse multi-scale features, and optimizing a depth estimation result; predicting three-dimensional Gaussian primitive parameters; and constructing a Gaussian field to generate a new visual angle image. According to the method, the problems of low accuracy, integrity and efficiency of existing sparse view angle three-dimensional reconstruction are solved. According to the invention, the precision and the detail fidelity of the depth map are improved, and high-fidelity three-dimensional reconstruction under the sparse visual angle condition is realized.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention discloses a lightweight image super-resolution reconstruction method based on high-frequency enhanced CNN-Mama adaptive fusion, and relates to the field of image super-resolution and image processing, and the technical field of computer vision and deep learning. Comprising the steps of obtaining an original image data set for data preprocessing, and constructing a training data set; a lightweight image super-resolution reconstruction model based on a high-frequency enhancement CNN-Mama adaptive fusion module is constructed; training the lightweight image super-resolution reconstruction model by using the training data set to obtain a trained image super-resolution reconstruction model with a weight; and performing super-resolution reconstruction on the target low-resolution image through the image super-resolution reconstruction model to obtain a corresponding super-resolution image. According to the method, on the basis of a high-frequency enhancement CNN-Mamba adaptive fusion module, organic fusion of global and local features is realized, and the comprehensive processing capability of the model on image features is improved.

The invention belongs to the field of medical image processing, relates to a medical image focus segmentation and three-dimensional reconstruction method and system based on artificial intelligence, and aims to solve the problem of low model precision caused by mutual isolation of segmentation and reconstruction and unidirectional transmission of errors. The method comprises the following steps: fusing a multi-modal medical image; a segmentation network is adopted to generate a preliminary focus mask; constructing an initial three-dimensional geometric surface based on the mask, and performing physically-driven curved surface optimization; reversely projecting the optimization model to the feature space of the segmentation network, calculating the spatial inconsistency between the optimization model and network prediction, and generating an attention weight map; feeding back the attention weight map to the segmentation network, and iteratively updating network parameters to refine segmentation boundaries; and based on the final segmentation result after convergence, three-dimensional reconstruction guided by the network features is executed again. According to the method, a closed-loop feedback and collaborative optimization mechanism between segmentation and reconstruction is constructed, and the accuracy of focus segmentation and the geometric fidelity of a three-dimensional reconstruction model are remarkably improved.

The invention discloses a rapid high-fidelity reconstruction method for an automatic driving scene, and belongs to the technical field of computer vision. The invention aims to solve the problems of excessive model parameters and low rendering efficiency in the existing automatic driving scene reconstruction process. Comprising the following steps: decomposing a dynamic driving scene into a static background and a dynamic foreground; establishing a three-dimensional Gaussian foreground model under a local coordinate system, converting the three-dimensional Gaussian foreground model to a world coordinate system, and splicing the three-dimensional Gaussian foreground model with a three-dimensional Gaussian background model under the world coordinate system to obtain a whole scenic spot cloud; carrying out densification pruning and refining pruning to obtain a final full-scene spot cloud; and an ETB-Box method and an MITS method are adopted to optimize a three-dimensional Gaussian rendering pipeline of the final point cloud of the whole scene, a three-dimensional Gaussian accurate tile corresponding to each point cloud is calculated, the three-dimensional Gaussian is rendered, and high-fidelity reconstruction of the automatic driving scene is realized. According to the invention, rapid high-fidelity reconstruction of the automatic driving scene is realized.

The invention discloses a sparse view angle indoor reconstruction method based on uncertainty perception depth supervision. The method comprises the following steps: acquiring multi-view angle image data; solving a camera pose based on the SFM; a neural radiation field model based on uncertainty perception is constructed, and modeling is carried out on volume density, color and depth uncertainty parameters of space points; introducing a depth uncertainty synthesis formula into the volume rendering framework; constructing luminosity loss fused with random structure similarity; designing a self-adaptive deep optimization mechanism of uncertainty perception, and optimizing a training process through a progressive uncertainty learning strategy; and applying the trained model to a sparse view angle indoor scene to generate a high-quality three-dimensional reconstruction result and an uncertainty quantization graph. According to the invention, by introducing a fusion method of uncertainty perception and adaptive depth supervision, the problems of poor reconstruction quality and detail missing under a sparse view angle are effectively solved, and the precision and robustness of indoor scene three-dimensional reconstruction are significantly improved.

The invention discloses an indoor scene three-dimensional reconstruction method based on deep fusion and confidence modeling, and belongs to the technical field of computer vision. According to the method, composite data frames such as a color image, a depth map, an IMU (Inertial Measurement Unit) and a camera attitude are comprehensively utilized to carry out regional three-dimensional reconstruction on an indoor scene: firstly, the scene is divided into a smooth region (such as a wall, a ground, a ceiling, a glass plane, a mirror surface, a blackboard and other planes) and a complex curved surface region; aiming at the smooth area, adopting geometric prior guide plane fitting provided by a visual large model, and combining sensor attitude information to quickly reconstruct a regular plane model; for a complex curved surface area, a multi-frame point cloud fusion strategy is adopted to accumulate different view angle information, and a deep residual error refining network is utilized to recover curved surface details, so that a high-precision curved surface model is obtained. According to the method, the three-dimensional structure of the indoor scene can be efficiently reconstructed, the global framework of the smooth area is reserved, and the details of the surface of a complex object are depicted in detail.

The invention discloses a point cloud reconstruction method and system based on three-dimensional Gaussian sputtering, and is used for solving the technical problem that the structural stability of a final three-dimensional point cloud model is not good enough due to the fact that a traditional point cloud reconstruction method causes gradient propagation abnormity, and the optimization process is not convergent or falls into a local minimum value. The method comprises the following steps: firstly, acquiring a multi-view image and a reference view image, and constructing a covariance degradation risk probability graph; generating a plurality of Gaussian three-dimensional points to be regulated and controlled, performing three-dimensional point screening and fitting credibility score calculation, outputting secondary regulation three-dimensional points and corresponding scores, and constructing an initial three-dimensional point cloud model; performing secondary adjustment on the three-dimensional point by combining the multi-view image and fractional optimization to obtain a target Gaussian three-dimensional point, and updating the initial model into an intermediate model; and updating the intermediate model through a covariance updating gating mechanism based on gradient convergence dynamic monitoring, and outputting a target three-dimensional point cloud model.

The invention discloses an agricultural scene three-dimensional reconstruction method based on multi-modal sensor fusion, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring multi-modal data of a target scene based on a tracked robot, wherein the multi-modal data comprises image data acquired by a binocular camera, point cloud data acquired by a laser radar, time sequence inertial data acquired by an inertial measurement unit and position data acquired by a Beidou positioning system; extracting different modal data features in the multi-modal data, and carrying out heterogeneous fusion on the different modal data features to obtain multi-modal data fusion features; and agricultural scene three-dimensional reconstruction is carried out by using a three-dimensional dynamic construction method based on space-time-semantic joint embedding. According to the invention, the environment scene modeling precision of the agricultural robot under the interference of muddy, raised dust and vibration can be improved.

The invention relates to the technical field of satellite communication signal processing, and discloses a broadband satellite signal blind demodulation reconstruction method and system based on sparse representation. The method comprises the following steps: carrying out frequency domain transformation on broadband satellite mixed signals to extract pilot frequency features to construct a beam state feature matrix, inputting the matrix into an adversarial network to generate a beam adaptive sparse dictionary, constructing a topological relation graph according to a Doppler frequency shift set, and carrying out collaborative sparse decomposition to obtain a sparse coefficient matrix; and extracting a code rate candidate set to execute parallel sparse reconstruction, determining an actual code rate through self-consistency verification to obtain a complete signal, analyzing a switching instruction, extracting time sequence statistical characteristics, predicting target domain characteristics, generating a switched sparse dictionary, and executing demodulation. The sparse decomposition precision, the code rate blind estimation accuracy, the cooperative processing efficiency and the switching continuity of the broadband satellite mixed signals are improved.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.