1010 results about "Radiation field" patented technology

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

The invention discloses an indoor real scene three-dimensional reconstruction method based on improved 3D Gaussian sputtering, belongs to the technical field of three-dimensional reconstruction, and aims to complement initial point clouds in a self-adaptive manner through a designed feed-forward point cloud densification network, generate a high-density 3D Gaussian radiation field, fill scene missing details and avoid the problem of precision attenuation caused by lack of point cloud constraints. In the cost body optimization stage, a depth regularization loss module is designed by integrating global structure features, depth information is integrated into the optimization process, algorithm reasoning is accelerated, and object structure fuzziness is relieved; and finally, in combination with adaptive density control and Gaussian pruning, radiation field parameters and density distribution are finely adjusted, indoor scene reconstruction with double improvement of visual fidelity and structural integrity is realized, interactive application becomes possible, a real-time solution is provided for indoor scene reconstruction, and application of a three-dimensional reconstruction technology is widened.

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

The invention provides a three-dimensional space data analysis method and system based on deep learning, and relates to the field of computer vision, and the method comprises the steps: carrying out the modeling of a static scene fundamental model and a dynamic object motion track through a space-time separated dynamic nerve radiation field; a lightweight dynamic neural radiation field model is deployed at an edge computing node, multi-modal sensor data are processed in real time, local three-dimensional scene representation is generated, rendering and prediction computing of a neural radiation field are executed on the edge node, and the implicit feature difference quantity of scene change is uploaded to a cloud; and the cloud end aggregates feature difference data of multiple edge nodes through a federated learning framework, dynamically updates a global scene priori knowledge base and issues the global scene priori knowledge base to the edge nodes. The method can improve the processing efficiency, precision and applicability of the three-dimensional data, and is especially suitable for carrying out tasks such as object recognition, target detection and semantic segmentation in a complex environment.

A generalizable neural radiation field reconstruction method based on multi-modal information fusion, including: Step 1, constructing photometric features and geometric features based on unstructured multi-views, and constructing a multi-modal neural encoder by performing incrementally complementary fusion on the photometric features and the geometric features; Step 2, converting the multi-modal neural encoder and raw RGB pixel bodies of the unstructured multi-views into a volume density and radiation brightness; Step 3, sampling light on the basis of the constructed multi-modal neural encoder, aggregating context features of the sampled light based on a transformer network to obtain light context features; and Step 4, decoding, using the light context features, the volume density and the radiation brightness; rendering, based on the decoded volume density and the radiation brightness, to generate a free-view RGB-D image; and guiding dense reconstruction of a low-texture scene by combining photometric supervision and sparse geometric supervision.

The invention relates to the technical field of unmanned aerial vehicle control, provides a visual environment generation method, system and device based on a neural radiation field and a storage medium, and solves the problem of poor visual environment generation effect. The method comprises the steps that semantic segmentation is carried out on a corrected image, image data with semantic tags are generated, and the semantic tags are used for marking categories of objects in an outdoor scene; updating the neural radiation field model based on the image data with the semantic tag, mapping the semantic tag to a voxel feature space corresponding to the neural radiation field model, and constructing a semantic point cloud; identifying a non-key area in the multi-view image, performing redundant voxel cutting on the non-key area, and generating a cut semantic point cloud; and utilizing the clipped semantic point cloud to drive a neural radiation field model, and generating any visual angle scene of the outdoor scene. According to the technical scheme, lightweight semantic modeling and efficient free viewpoint rendering of the outdoor visual environment are achieved, and the new visual angle generation efficiency and quality of the complex environment are improved.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention relates to the technical field of electronic information, in particular to a multi-band cooperative interference gallium nitride phased-array antenna control method and system. The method comprises the steps of extracting key parameters of electromagnetic signals, and generating a cooperative interference strategy combination; mapping the frequency band optimization weight to a dynamic working mode of the gallium nitride power amplifier unit, adjusting the radiation pattern characteristics of the reconfigurable dipole array according to the power distribution parameters, and constructing a composite beam forming framework; phase weighting and spatial multiplexing are carried out on the interference signals of all the frequency bands based on the composite beam forming architecture, and a multi-beam interference cluster is generated; thermodynamic state data of the gallium nitride power amplifier module in the multi-beam interference cluster generation process are collected in real time, and heat dissipation parameters of the liquid cooling system are regulated and controlled according to the thermodynamic state data. Intelligent collaboration and accurate pointing of multi-band interference signals are realized, the interference coverage range and the energy utilization rate are improved, and an efficient interference system integrating electromagnetic sensing, multi-beam collaboration and thermal management is formed.

The invention relates to the technical field of safety monitoring of constructional engineering, in particular to an intelligent construction safety monitoring system and method based on multi-modal data fusion, and the system comprises a plurality of modules: a 5GMEC-based heterogeneous data space-time alignment module which completes coordinate system conversion between laser point cloud and a BIM model by using an improved Fast-ICP algorithm; the feature level fusion network comprises a geometric feature branch (improved unscented Kalman filter processing point cloud normal vector) and a time sequence feature branch (LSTM processing stress sensor data); the NeRFXL fusion engine is used for fusing the point cloud and the video data by adopting a multi-scale neural radiation field; the multi-task anomaly detector is used for constructing a hierarchical Transform architecture and introducing a modal gating mechanism; and the dynamic knowledge graph engine constructs a knowledge graph updating module based on a graph neural network, and all the modules are in communication connection with one another and work cooperatively, so that functions of data processing, fusion, anomaly detection, knowledge updating and the like are realized.

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 relates to the technical field of computer vision and three-dimensional space modeling, and discloses a real-time multi-instance segmentation method and device based on a Gaussian splash radiation field model. The method comprises the following steps: based on a two-dimensional Gaussian splash radiation field model, rendering a visual angle with continuous spatial change to obtain an image sequence, and obtaining a multi-visual-angle consistent two-dimensional instance segmentation mask of the image sequence; and assigning an instance tag to each Gaussian primitive based on the two-dimensional instance segmentation mask. And for a two-dimensional Gaussian splash radiation field model with an instance label, acquiring a coarse instance segmentation mask and a color image at any view angle by using a Gaussian splash algorithm, inputting the mask and the image into a lightweight post-processing network for edge detection and region connectivity repair, and outputting a target two-dimensional instance segmentation mask with a complete structure and a label consistent with a three-dimensional scene. The method does not depend on any training or distillation process, directly acts on a Gaussian splash radiation field model, and has the advantages of high reasoning speed, high semantic consistency, support of multi-target continuous tracking and the like.

The invention provides a virtual reality interaction and content generation method and system based on a large model. According to the method, original point cloud data are collected to be aligned with semantic tags, and a topological graph containing object positions, categories and spatial constraints is constructed; dynamically calibrating the point cloud and mapping the point cloud to a unified coordinate system, and extracting a shielding boundary to generate a parallax mapping table; recognizing a blank area delimited by a user through a gesture, and generating a new object parameter candidate set of physical compliance in combination with the topological graph; screening parameters by using a large model, and reserving physical effective parameters after collision response, gravity and illumination verification; and generating the geometry and texture of the virtual object in the occlusion area based on the incremental neural radiation field, optimizing the depth and the rendering precision in combination with the mapping table, outputting the depth and the rendering precision to an interactive interface, and updating topological parameters. According to the method and the device, a new object which is physically consistent and rendered and optimized is dynamically generated based on user interaction in a virtual reality scene, and the sense of reality and interaction experience are improved.

The invention provides a NeRF sparse view reconstruction method based on adaptive multi-modal feature fusion, and relates to the technical field of three-dimensional scene reconstruction. The method comprises the following steps: extracting a depth feature and a semantic feature from a sparse input view, and generating a first multi-modal feature through a feature enhancement network; a mixed attention mechanism is combined with channel and space attention to perform dual selective enhancement on the features to obtain second multi-modal features; the weight distribution network based on confidence guidance dynamically fuses the multi-modal features, harmonizes conflicts among the modals through a consistency loss function, and generates final fusion features; and inputting the fusion features into an enhanced neural radiation field rendering module, decomposing a scene into multi-scale representation by using layered feature coding, and optimizing geometry and appearance details in stages by combining a progressive training strategy to realize high-quality three-dimensional reconstruction. According to the method, the problems of geometric distortion, fuzzy texture and inconsistent modes caused by sparse views in a traditional method are solved.

The invention provides a radiation field high-precision reconstruction method based on dynamic geometry aggregation, and relates to the technical field of radiation field reconstruction and computational physics crossing. The method comprises the following steps: extracting a corresponding attenuation coefficient as a priori value; generating a Gaussian radiation element at the position of the monitoring point; calculating the radiation intensity of any point in the space; suppressing mutation of attenuation coefficients of adjacent primitives; gathering to a high dose zone or dispersing to a low dose zone; and generating three-dimensional radiation dose rate distribution by adopting a differentiable split rendering pipeline, and calculating the radiation intensity of any query point in real time by outputting a parameterized Gaussian primitive set. Through the dynamic Gaussian primitive aggregation-physical constraint optimization dual-drive reconstruction framework, the radiation field distribution can still be accurately restored under the sparse detector condition, the error is greatly reduced compared with the traditional method, and the calculation efficiency is obviously improved.

The invention discloses a flexible surgical instrument-oriented multi-source sensing data adaptive fusion method and a related device. The method comprises the following steps: acquiring optical, mechanical and pose multi-mode sensing data based on a timestamp synchronization technology in a working environment; clock compensation and space coordinate transformation based on an instrument kinematics model are carried out on the multi-modal data, and space-time registration is completed; inputting the registered data into a neural radiation field model to generate continuous space implicit scene representation; inputting the implicit representation into a pre-trained RWKV fusion network to obtain a fusion feature; and superposing the fusion feature and the original low-frequency component through residual connection, and integrating to generate three-dimensional voxelization environment state data. According to the method, multi-source information high-fidelity fusion is achieved through a single path, data redundancy is remarkably reduced, and the real-time performance and precision of intra-operative environment perception are improved.

The invention discloses a high-precision visual angle dependent appearance reconstruction method based on a neural radiation field, and relates to the technical field of artificial intelligence computer vision and three-dimensional reconstruction. The method at least comprises the following steps: S1, carrying out colmap processing on a shot multi-view image of an object to be reconstructed to obtain a corresponding camera pose; s2, building a network architecture used for calculating a non-explicit visual angle dependent color, i.e., VD-NeRF; and S3, based on the established VD-NeRF, measurement verification is carried out on the VD-NeRF by using the test set, model training is completed, and appearance reconstruction is carried out based on the trained model. Compared with other methods, in particular in the aspects of processing image blurring and incapable of accurately capturing the view angle dependent object surface, the VD-NeRF effectively improves the image detail reduction capability and the modeling performance of view angle dependent characteristics by combining the attention mechanism and the NeRF.

The invention relates to the technical field of medical image processing and surgical navigation, in particular to a lung cancer intraoperative navigation and positioning method based on three-dimensional reconstruction and a deep neural network, which comprises the following steps of: based on multi-source optical and acoustic fusion, performing fractal wavelet multilayer decomposition on preliminarily purified data acquired in an operation, and performing multi-source optical and acoustic fusion; multi-modal characteristics of optical and acoustic channels and fractal measurement are obtained; using quantum heuristic multi-agent search to find a potential focus or a key area with serious deformation, and performing interpolation completion on a defect or noise part in a nerve radiation field; performing elastic deformation registration on the interpolation result and the lung three-dimensional model, and calculating a nonlinear deformation field and intra-operative tumor prediction coordinate data; and superposing the deformed lung model to an augmented reality environment through external tracking information to display the focus position. And when lung collapse or the traction amplitude exceeds the limit is monitored, searching and registration are repeated for real-time updating, and accurate navigation positioning under dynamic deformation of the lung is realized.

The invention discloses an underwater three-dimensional scene reconstruction method and device based on a three-dimensional Gaussian splashing and underwater imaging model. The method comprises the following steps: constructing an underwater truth value image, corresponding camera parameters and sparse initialization point cloud into a data set; training a first underwater radiation field based on the three-dimensional Gaussian splash model and the underwater imaging model by using the data set and the three-dimensional reconstruction loss function, and training a second underwater radiation field based on the three-dimensional Gaussian splash model and the underwater imaging model by using the data set, the three-dimensional reconstruction loss function and the self-trimming supervision loss function; and generating a water-containing underwater image under the new visual angle by using the trained three-dimensional Gaussian splash model in the first underwater radiation field and the underwater imaging model, and generating a water-free underwater image under the new visual angle by using the trained three-dimensional Gaussian splash model in the second underwater radiation field. The method has the advantages that the training speed is high, the image rendering and three-dimensional reconstruction quality is high, and the underwater image with water or without water can be selectively rendered.

The invention relates to an asteroid global terrain three-dimensional reconstruction method for illumination variation, and the method comprises the following steps: inputting an asteroid optical image, estimating the pose of a camera, generating a sparse point cloud, and initializing a Gaussian radiation field; using the image segmentation large model to output a segmentation mask of the asteroid optical image, and pruning the Gaussian radiation field; identifying a shadow region on the surface of the asteroid by combining a # imgabs0 # mixed mask output by a Gaussian radiation field; the camera position, the spherical harmonic coefficient and the Hash code are combined with the angle of the incident light to be input into a neural network, and color values of Gaussian primitives of illumination and shadow areas are regressed; depth normal consistency loss and rotation variance and scale constraint loss are added, optimization of a Gaussian radiation field is jointly supervised in combination with luminosity loss, a Gaussian opacity field is calculated, and a three-dimensional model of the asteroid global terrain is extracted. According to the method, the shadow region on the asteroid can be identified, and the three-dimensional reconstruction precision of the asteroid with surface illumination variation is improved.

The invention relates to the technical field of visual perception, and discloses a mechanical arm visual grabbing method and system. Comprising the following steps: collecting depth images of a target scene at different visual angles in real time, and generating initial point cloud data by using all the depth images; processing the initial point cloud data by using a double-flow neural radiation field to generate a geometric field and a capture field; a preset dynamic updating mechanism is adopted, combined dynamic optimization is conducted on the geometric field and the grabbing field, and optimal grabbing parameters corresponding to all moments in the process that the mechanical arm executes the current grabbing task are obtained; and the mechanical arm is controlled to execute the grabbing action based on the optimal grabbing parameters corresponding to all the moments. According to the method, high-precision three-dimensional scene representation is constructed through depth fusion of multi-view depth images, so that the integrity and robustness of geometric reconstruction of an object in a complex environment can be remarkably improved; based on joint modeling of a double-flow nerve radiation field, collaborative optimization of a three-dimensional structure and grabbing parameters of an object is achieved, and stability and adaptability of grabbing performance in a dynamic environment can be effectively improved.

The invention provides a robot indoor navigation method and system, and the method comprises the steps: obtaining an RGB image frame sequence collected by a camera, and estimating a camera pose and a sparse three-dimensional landmark point of an image frame through a depth block visual odometer; based on the camera pose and the sparse three-dimensional landmark point, dense geometric enhancement is carried out on the image frame through a deep neural network of a transformer architecture to obtain dense geometric prior information; training a neural radiation field according to the initial camera pose, the dense geometric prior information and the image frame data, and performing joint optimization on neural radiation field parameters and the camera pose based on luminosity consistency, geometric consistency and camera pose constraints; based on image frames collected in real time, novel view synthesis is carried out through the optimized nerve radiation field, and robot navigation is carried out based on a novel view. According to the scheme, accurate estimation of the camera pose and high-quality reconstruction of the three-dimensional scene can be realized, the computing resource overhead is low, and the real-time performance can be guaranteed.

The invention discloses a power distribution station multi-point environment control system and method, and relates to the technical field of power system automation, and the method comprises the following steps: building a time-frequency phase-locked observation baseline based on the scattering relation between an infrared band and water vapor under the condition of sudden humidity increase, inverting aerosol particle size distribution and concentration distribution, and generating a multi-dimensional scattering phase residual field; according to the multi-dimensional scattering phase residual field, a real radiation field is reconstructed by adopting multi-path reverse ray variation solution and combining polarization dual-angle scanning, and a reflection saturation risk spectrum is generated. According to the method, a real radiation field is reconstructed through multi-dimensional observation and inversion, a consistency graph is generated by fusing multi-source data, a misjudgment boundary is accurately identified, a control margin and a switching condition are constructed based on residual playback and time correction, and a steady-state closed-loop control chain is formed in combination with a dynamic regulation and control mechanism. The self-adaptive precise regulation and control of the power distribution station environment and the stability guarantee of the thermal field are realized.

The invention provides a pulse passing rate improving method based on accumulation signal demodulation, and belongs to the technical field of nuclear electronics digital pulse processing. Comprising the following steps: performing baseline deduction and monopulse screening on a radiation field pulse signal; constructing a nuclear pulse signal model, fitting to obtain model parameters, and constructing a system response matrix; modeling a nuclear detection physical process into a mathematical model; solving a constraint optimization problem by using a non-negative least square method; and demodulating and calculating all the pulse signals including the stacked pulses which are discarded by stacking, so as to obtain delta pulses containing energy information. According to the invention, the accumulation signal which is abandoned by a traditional accumulation abandoning algorithm is demodulated, so that the technical problem of increasing the pulse passing rate is solved, and meanwhile, heavy peaks and high-energy trailing in an energy spectrum are inhibited. Compared with an existing accumulation discarding method, the pulse passing rate effect is remarkably improved on the premise that the energy spectrum accuracy is guaranteed.

The invention discloses a software development management method based on big data, and relates to the technical field of software development management, and the method comprises the steps: collecting multi-source development data, transmitting the multi-source development data to a data lake in real time through a distributed message queue, and generating a structured data stream with a timestamp and a time sequence relation graph; performing Monte Carlo sampling on the three-dimensional radiation field model of the neural radiation field and the causal influence graph, simulating the cascade influence submitted by the current code, and outputting a risk value and a dependency chain; loading a neural radiation field three-dimensional code space model, a node risk value and a dependency chain, dynamically rendering a causal influence path, and generating a visual risk report; and generating a code optimization task according to the visual risk report, submitting an optimized code version to a code library, and calibrating parameters of the neural radiation field three-dimensional code space model. According to the method, the spatio-temporal characteristics of code change are analyzed, Monte Carlo simulation is combined, hidden dependence and a risk conduction chain are identified, and the comprehensiveness and predictive ability of framework corruption analysis are improved.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention discloses a three-dimensional reconstruction method and system based on deep learning, and belongs to the technical field of computer vision and deep learning. An existing neural implicit method is poor in reconstruction result in a weak texture area and a non-Lambert surface. Traditional geometric supervision depends on noise data, resulting in incomplete surface; in order to solve the problems of low global and local feature fusion efficiency in the optimization process and the like, the multi-view image of a target object is obtained by using a camera; obtaining camera parameters and sparse point clouds corresponding to the multi-view image by using an SfM model; building a three-dimensional reconstruction model based on the neural radiation field, and obtaining a reconstruction result; and evaluating a reconstruction result, and comprehensively evaluating the reconstruction result through the performance indexes. According to the invention, the problems of fuzzy reconstruction and poor precision of the target object in the weak texture area are solved; the problem of data annotation is solved, data acquisition is simple, and extra effective supervision does not need to be introduced; the cultural relic heritage with the weak texture area can be converted into a digital model permanently stored in a computer.

The invention particularly relates to a three-dimensional radiation field modeling method for performing infrared factor reasoning through poses. The method comprises the following steps: acquiring a to-be-processed target spatial position and target visual angle information; performing coding processing on the target spatial position and the target visual angle information to obtain a high-dimensional spatial position code and a visual angle information code; carrying out splicing processing on the spatial position code and the visual angle information code to obtain a fusion feature code; inputting the fusion feature code into a trained infrared image generation model, obtaining an environment radiation parameter, an extinction coefficient, a temperature parameter and an emissivity parameter, and determining a self-radiation parameter based on the temperature parameter and the emissivity parameter; and performing volume rendering processing on the self-radiation parameter, the environment radiation parameter and the extinction coefficient to obtain a target infrared image. According to the method, physical variables such as temperature and materials can be adaptively updated and adjusted, so that more accurate radiation brightness values can be obtained at different positions and viewing angles.

The invention discloses a three-dimensional packaging modeling method based on three-dimensional Gaussian sputtering representation, and the method comprises the steps: firstly generating a geometric structure and surface texture of a three-dimensional packaging model through Gaussian distribution, and obtaining particle distribution; secondly, generating a particle attribute data set containing physical consistency parameters on the basis of particle distribution in combination with a physical simulation technology based on a particle system, globally optimizing the three-dimensional model by adopting a ray tracing and real-time rendering technology, and then combining rendered three-dimensional model features with multi-modal data input by a user to obtain a multi-modal data set; and generating an adaptive design template in combination with a text collaborative prompt learning algorithm. And finally, integrating the diffusion model and a neural radiation field NeRF technology, and generating a detailed three-dimensional packaging model based on physical consistency parameters and a modeling result in combination with a design template. According to the method, the user requirements can be accurately analyzed, accurate and efficient personalized customization is realized, and the user experience is enhanced.

The invention discloses a 3DGS-based integrated visual angle synthesis and sparse visual angle CT reconstruction method, which comprises the following steps: establishing an internal and external parameter matrix through X-ray scanning parameters, constructing an initial voxel space and an initialized 3DGS radiation field by combining sparse visual angle projection data and based on an ACUI strategy, modeling anisotropic radiation intensity by utilizing a radiation intensity response function, and reconstructing a 3D visual angle CT model. And generating a virtual projection by adopting volume consistency rendering. And the optimized radiation field is converted into a voxel grid, density contribution is applied to carry out voxel space reconstruction, a reconstructed voxel space is obtained, and a new 3DGS radiation field is generated. And re-rendering the virtual visual angle projection based on the new 3DGS radiation field, performing iterative optimization by using a loss function, and outputting a new visual angle synthetic image and a CT reconstruction body until a preset number of iterations is reached. And an efficient process of one-time training and dual output is realized. The efficiency and precision of X-ray imaging are improved, the radiation dose of a patient is reduced, and meanwhile, a higher-quality imaging solution is provided for medical diagnosis.

The invention provides a forging forming method and system for an aluminum alloy pipe target based on image processing, and relates to the technical field of image processing.The method comprises the steps that a three-dimensional digital grid model and a topological relation graph are established; a dual-spectrum camera array is adopted to obtain multi-view images and fuse the multi-view images; reconstructing a temperature radiation intensity mapping image through the neural radiation field network; executing image enhancement processing; extracting contour feature data by using a multi-scale texture decoupling network, and separating reflection, oxidation and forging components; a deformation trend is predicted based on the topological relation graph and the contour feature data, and a forging parameter adjusting instruction is generated; and the forging equipment is controlled according to the instruction and optimized in real time until the forming quality reaches the standard. According to the method, accurate monitoring and intelligent control of the aluminum alloy pipe target forging process are achieved.