376 results about "Volume rendering" patented technology

The invention relates to a method of visualising a dynamic anatomical structure (1), a computer program and a user interface. The method comprises (a) providing a sequence of three-dimensional medical images (M1, M2, M3, . . . MZ) of a dynamic anatomical structure (1) spanning a time period (T), (b) providing a dynamic model (14), in particular surface of the anatomical structure, (c) determining a volume of interest (40) containing an anatomical feature of interest (3) within each of the three-dimensional images, wherein the volume of interest (40) follows the position and / or the shape of the anatomical feature of interest (3) across the time period and wherein the volume of interest (40) is smaller than the complete field of view of the three-dimensional medical images (M1, M2, M3, . . . MZ), and (d) providing a three-dimensional visualisation environment (50, 70), wherein a visualisation (45) corresponding to a particular point in time comprises (i) a volume rendering of the volume of interest (40) of the three-dimensional image; and (ii) a visualisation of the dynamic model (14) in the same coordinate system. Preferably, the three-dimensional visualisation environment (50, 70) allows for displaying the dynamic model (14) and the volume rendered volume of interest (40) for each three-dimensional image across the time period in cine mode.

The invention belongs to the technical field of tree three-dimensional reconstruction, and particularly discloses a tree neural rendering and reconstruction method based on a sparse view, and the method comprises the steps: collecting an RGB image of a tree through a camera, obtaining camera parameters, inputting the camera parameters into a pre-trained depth estimation model, generating a rough depth map, and inputting the rough depth map into a neural radiation field network; introducing a cone sampling strategy, integrating a position coding method, a coding annealing strategy, depth sorting and depth consistency constraint, and extracting point cloud and picture features in the picture; carrying out implicit surface reconstruction based on volume rendering; introducing color constraint and geometric constraint based on color deviation and geometric deviation of implicit surface reconstruction; and a three-dimensional model of the tree is reconstructed from the implicit surface by using a Marking Cubes algorithm. By adopting the technical scheme, tree rendering is carried out based on the neural radiation field, tree reconstruction is carried out based on body rendering, and the reconstructed tree model can better retain and reproduce original geometric features and real textures of the tree.

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 belongs to the technical field of three-dimensional reconstruction in computer graphics, particularly relates to a sparse view outdoor scene three-dimensional reconstruction method based on multi-view stereo and grids, and aims to solve the problems that input depends on dense views, rendering results are prone to generating fuzzy artifacts, large-scale scene memory occupation is high and the like in the existing reconstruction technology. The method is especially suitable for high-precision reconstruction of an outdoor boundless scene under sparse view input. The method comprises the following implementation steps: acquiring a multi-view image of a scene to be reconstructed and a corresponding camera pose, and determining a scene center based on a K-means clustering algorithm; extracting input view features, generating a cost body based on a multi-view three-dimensional framework, and obtaining scene depth distribution priori by combining a probability depth estimation method; guiding voxel grid sampling based on depth distribution, and mapping light rays exceeding a grid boundary into a limited grid range by using a spatial contraction function; extracting image local features and voxel grid global features for adaptive fusion; and finally, generating a reconstructed image through volume rendering.

The invention discloses an implicit coding and geometric prior-based indoor three-dimensional scene reconstruction method and system. The method comprises the following steps of: performing data processing on image data to be reconstructed to obtain corresponding pre-processed data; calculating rays under a world coordinate system corresponding to pixels needing to be trained on the image data according to internal and external parameters of the camera; inputting a three-dimensional coordinate code and an observation angle code of a sampling point on a ray and an implicit appearance code of a corresponding image into the neural radiation field model to obtain a predicted density value and a predicted color value, and performing volume rendering prediction based on a symbolic distance function to obtain a pixel value so as to obtain a trained neural radiation field model; and obtaining a standard implicit appearance code based on the density value and the color value, and inputting data at least comprising the standard implicit appearance code into the trained neural radiation field model to obtain scene geometric information. According to the method, the dependence of the existing reconstruction scheme on the three-dimensional consistency of the acquired data can be reduced, and the robustness and representation capability of the algorithm in a complex scene can be improved.

The invention relates to a three-dimensional low-illumination scene brightening method based on 3D Gaussian sputtering, computer equipment and a program product, the three-dimensional low-illumination scene brightening method comprises the following steps: a plurality of fulcrums are arranged in a three-dimensional space, the attributes of the fulcrums comprise fulcrum position attributes and Gaussian point positioning attributes used for determining the positions of Gaussian points associated with the fulcrums, and the attributes of the fulcrums comprise the fulcrum position attributes and the Gaussian point positioning attributes used for determining the positions of the Gaussian points associated with the fulcrums; the attribute determining module is used for determining appearance attributes and geometric attributes of Gaussian point features associated with fulcrums; extracting reflection components and illumination components participating in rendering according to the appearance attributes; optimizing a fulcrum position attribute, a Gaussian point positioning attribute, an appearance attribute and a geometric attribute according to the difference between the 2D image and the 3D Gaussian rendering image under the corresponding view angle; extracting a gamma value from the appearance attribute by using a multi-layer perceptron to obtain an enhanced illumination component; and multiplying the reflection component by the enhanced illumination component to obtain a Gaussian point color, obtaining brightness enhanced images under different visual angles through volume rendering, and obtaining a brightness enhancement loss optimization multilayer perceptron according to the brightness enhanced images.

The invention discloses a neural radiation field rendering method and device based on a neural base and tensor decomposition, and belongs to the technical field of computer vision, and the method comprises the steps: collecting a multi-view image and camera parameters thereof to construct a data set; the method comprises the following steps: sampling a radiation field subjected to tensor decomposition according to camera parameters under a selected training view angle to obtain a density feature tensor and a texture feature tensor, respectively converting the density feature tensor and the texture feature tensor into a density value and a neural base, calculating an affine matrix and bias through a coefficient network based on the neural base, and performing affine transformation in combination with an observation direction to obtain a coefficient; inputting the neural bases and the coefficients into a neural hybrid network to obtain a color of a single sampling point, calculating a pixel color by using a volume rendering technology, and training the whole network; and setting a new visual angle, obtaining corresponding camera parameters, and performing image rendering by using the trained network parameters. According to the method, a high-quality three-dimensional scene can be efficiently reconstructed from multi-view image data, a complex surface reflection effect is effectively reconstructed, and vivid image rendering under a new view is realized.

The invention belongs to but is not limited to the technical field of three-dimensional space dose visualization, and discloses a three-dimensional nuclear emergency radiation field calculation and visualization method and system, based on a point nuclear method, a cumulative factor, mass attenuation factor and flux rate-dose rate conversion factor database is established, a mean free path solving algorithm is optimized, and a three-dimensional nuclear emergency radiation field is obtained. The geometric types of radioactive sources are expanded, and the dose calculation efficiency is improved through parallel calculation; plane dose rate drawing at a certain height is realized based on a two-dimensional texture mapping method; three-dimensional space dose rate contour surface drawing is realized based on a moving cube algorithm; and three-dimensional space dose rate volume rendering is realized based on a three-dimensional texture volume rendering technology. Radiation field dose calculation, three-dimensional radiation field dose visualization and GIS are integrated, and the system is used as a subsystem of a nuclear emergency aid decision simulation platform. The dose distribution diagram in the area can be judged more visually, and the method has a good help effect on nuclear emergency command.

The invention provides a dynamic three-dimensional scene reconstruction and rendering system and method and related equipment, and the method comprises the steps: receiving a monocular video sequence through an image encoder, and carrying out the visual feature extraction of each frame of image; the scene generator constructs a three-plane structure expression aligned with the view angle of the current camera based on the multi-frame visual features extracted by the image encoder, performs time coding on the multi-frame visual features and maps the multi-frame visual features to the three-plane structure expression to generate a three-dimensional scene feature expression; and the scene decoder decodes the three-dimensional scene feature representation and generates a composite image under a corresponding view angle based on a volume rendering mechanism, and the composite image can display geometric details of the scene under different view angles and maintain geometric consistency under different view angles. Modeling and rendering of the dynamic three-dimensional scene are achieved through the monocular video sequence, multi-view input or prior scene information does not need to be depended on, and the adaptive capacity and generalization performance of the system in a rapid change environment are effectively improved.

The invention belongs to the field of computer vision, image processing and space engineering, and discloses a lunar soil test piece in-situ three-dimensional reconstruction method based on an improved neural radiation field algorithm, and the method comprises the steps: obtaining a multi-view image set of a lunar soil test piece, and carrying out the self-adaptive adjustment of lunar surface illumination; the pose of the high-resolution camera is calculated through multi-view stereoscopic vision, and a depth map is obtained; the method comprises the following steps: generating a neural coding volume based on a Vision Transform model, and extracting a multi-dimensional feature vector; generating a neural radiation field, and predicting rendering features of the volume points; carrying out micro-volume rendering, and carrying out loss feedback optimization by using color rendering, depth and illumination; performing grid extraction by using a moving cube algorithm to obtain a three-dimensional grid model; for feature extraction difficulty and reconstruction mismatching difficulty caused by lunar soil background texture similarity, the problem of low reconstruction precision under a lunar surface non-uniform illumination condition is effectively solved, and high-quality and high-precision in-situ rapid three-dimensional reconstruction of a lunar soil test piece 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 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 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.

To compute an image of a dynamic 3D scene comprising a 3D object, a description of a deformation of the 3D object is received, the description comprising a cage of primitive 3D elements and associated animation data from a physics engine or an articulated object model. For a pixel of the image the method computes a ray from a virtual camera through the pixel into the cage animated according to the animation data and computes a plurality of samples on the ray. Each sample is a 3D position and view direction in one of the 3D elements. The method computes a transformation of the samples into a canonical cage. For each transformed sample, the method queries a learnt radiance field parameterization of the 3D scene to obtain a color value and an opacity value. A volume rendering method is applied to the color and opacity values producing a pixel value of the image.

The invention discloses a low-illumination scene image three-dimensional reconstruction method and device based on a 3D Gaussian ball, and electronic equipment. The method comprises the following steps: 1) obtaining a low-illumination scene image; 2) initializing information of a 3D Gaussian ball in a 3D space according to camera parameters and a volume rendering technology to obtain an initial position, an initial radius and an initial color of the 3D Gaussian ball, and writing the initialized 3D Gaussian ball information into the enhanced image; 3) constructing an illumination adjustment network based on the convolutional neural network fused with the attention mechanism; 4) inputting the enhanced image obtained in the step 2) into an illumination adjustment network, and predicting an illumination weight value of each 3D Gaussian ball by using the illumination adjustment network; and 5) based on the initial information of the 3D Gaussian ball and the illumination weight value, performing three-dimensional scene reconstruction by using a three-dimensional reconstruction algorithm to obtain a three-dimensional scene reconstruction view Cnor under normal illumination. According to the invention, the illumination can be dynamically adjusted through the illumination adjusting network, and the real restoration of the low-illumination scene is realized.

The invention relates to the technical field of 3D model construction, and discloses a sparse view angle 3D model construction method and device, equipment and a storage medium, and the method comprises the steps: obtaining sparse view angle images of different view angles of a target object; performing 3D modeling on the target object by using the sparse view angle image to obtain a corresponding 3D point cloud and a pose of each image; constructing a neural radiation field model used for predicting an SDF field and an RGB field, and performing joint supervision training on the neural radiation field model by adopting a volume rendering mode based on the sparse view angle image and the pose and the 3D point cloud thereof to obtain a trained neural radiation field model; and inputting a preset resolution into the trained neural radiation field model to obtain a corresponding SDF value and a corresponding RGB value, and performing explicit 3D of an implicit scene by using a preset algorithm to obtain a corresponding scene-level or individual-level 3D virtual simulation model, and the method provided by the invention can realize accurate and efficient 3D model reconstruction only by using a sparse view angle image.

The invention discloses a geometric spectrum coupling-based neural radiation field high-fidelity representation method and system. The method comprises the following steps of: obtaining sparse multi-view image data and calculating internal and external parameters of a corresponding camera; generating light based on internal and external parameters of the camera, and performing point sampling on the space along the direction of the light to obtain sampling points; constructing a differentiable coding layer, predicting the density of sampling points through forward propagation of a neural network, and obtaining a principal curvature, a density gradient and an included angle between a normal vector of the sampling points and an observation direction based on a density prediction result; constructing a spectrum-geometric coupling function, and calculating to obtain a spectrum coupling coding vector; the spectrum coupling coding vector is used as an input to be transmitted into a NeRF main network, and NeRF volume rendering is carried out according to a density and color prediction result; and performing difference optimization training on the rendering result and the real image to obtain a high-fidelity composite image. According to the invention, the expression ability of the neural radiation field model in the high-frequency detail area can be enhanced.

The invention relates to an X-ray fluoroscopic image generation method and device and an X-ray fluoroscopic examination system. The generation method comprises the steps that a target sight vector for a target organ is determined according to observation requirements and the target position where the target organ of a living body needing to be detected is located; determining a primary rotation angle and a secondary rotation angle of an imaging arm in the X-ray fluoroscopy equipment based on the target sight line vector, and determining a primary rotation matrix and a secondary rotation matrix; determining a target projection matrix conforming to a target projection geometrical relationship based on the primary rotation matrix and the secondary rotation matrix; and based on the target projection matrix and pre-acquired three-dimensional computed tomography (CT) data of the target organ, performing volume rendering to obtain a two-dimensional target X-ray perspective image under the target observation view angle. According to the generation method and device of the X-ray perspective image and the X-ray perspective inspection system, the X-ray perspective image under the expected observation view angle can be freely generated based on the three-dimensional CT data obtained in advance.

The invention belongs to the field of virtual image display, particularly relates to a spherical reflection cavity virtual calibration method based on a neural radiation field, and aims to solve the problems that micron-level deviation is difficult to accurately capture due to interference of a high-reflection surface, and an executable correction instruction cannot be generated by fusing process constraints. The method comprises the following steps: deploying image acquisition equipment around a cavity to synchronously capture a multi-view optical image sequence; inputting a neural radiation field model, reconstructing a three-dimensional geometric structure and optical properties through a volume rendering technology, and dynamically optimizing and fitting reflection characteristics and curvature changes of a carbon fiber material; extracting spherical radius, local curvature and boundary contour parameters, and comparing the parameters with design specifications point by point to generate a deviation distribution diagram; generating a multi-level calibration instruction set based on the deviation distribution diagram in combination with the laying layer tolerance and the assembly clearance; and outputting an instruction to the manufacturing system to execute physical adjustment, and re-collecting the image to verify the calibration effect until the acceptance index is met. According to the invention, the deviation capturing precision is improved, and a correction instruction can be generated.

The invention provides a component freezing recovery method based on a page life cycle interface. In the frame compiling stage, page life cycle event monitoring logic is injected into the component, a freezing and recovering event monitor is automatically registered, a large-data-volume rendering component tree is recognized, a freezing identifier is added, and butt joint of the Vue component tree and a browser freezing event is achieved. When a freezing event is triggered, dependence of a responsive memory for releasing a virtual rolling component in a background label page is ensured by pausing virtual rolling rendering, generating a component state snapshot, creating a rolling position anchor point record, removing non-visual area nodes, retaining placeholder elements and releasing responsive data binding. And after a recovery event is triggered, positioning a rolling position based on anchor point parameters, preferentially reconstructing a visual area component in stages, and recovering the responsive system, so that rolling position zero offset and state zero loss are realized when the page is recovered, and the reconstruction process has no perception delay. According to the method, manual intervention is not needed, and front-end development efficiency and performance optimization are both considered.

The invention discloses a three-dimensional packaging modeling method based on NeRF and a diffusion model, and the method comprises the steps: firstly receiving demand description information, fusing packaging physical constraint conditions for joint coding, and forming a comprehensive condition vector; secondly, a cross-path dynamic information exchange mechanism is established through a geometric generation path and a texture optimization path, an initial three-dimensional packaging model is generated, physical consistency constraints are added, and NeRF is used for conducting volume rendering reconstruction on the initial three-dimensional packaging model; and then introducing an observation distance adaptive dynamic scale perception neural radiation field optimization mechanism, and generating a multi-scale NeRF model after scale perception optimization. And finally, establishing mapping of physical attributes and optical characteristics of the packaging materials, and generating a final three-dimensional packaging model through independent optical calculation and physical synthesis processes of the multi-layer materials. According to the method, the semantic understanding deviation of CLIP coding in vertical field application is solved, and the material reality representation of industrial-grade three-dimensional packaging modeling is realized.

The invention discloses a sparse view angle cultural relic three-dimensional reconstruction method, a storage medium and computer equipment. The method comprises the following steps: acquiring a cultural relic multi-view angle image and camera parameters; constructing a ray and carrying out spatial point sampling on the ray; joint modeling is carried out on the uncertainty of the spatial points, and color, volume density and evidence uncertainty parameters are output in the implicit neural radiation field model at the same time; a progressive sine position coding method based on uncertainty driving is introduced, a low-frequency channel is activated preferentially at the initial stage of training, and high-frequency features are unfrozen dynamically along with uncertainty feedback; after unfreezing is completed, continuous weighted optimization is carried out on the reliable and unreliable areas by utilizing a partition self-supervision consistency mechanism of uncertainty perception, and potential geometric information of the unreliable areas is fully utilized; and generating a high-fidelity three-dimensional reconstruction image and a confidence evaluation result of the cultural relic through volume rendering. According to the method, the cultural relic three-dimensional reconstruction precision and stability under the sparse view angle condition are effectively improved.

The present application relates to the technical field of human-computer interaction, and specifically to a holographic three-dimensional medical data visualization method and system capable of achieving mid-air gesture interaction. The method comprises the following steps: firstly, acquiring user environment information and user motion information, wherein the user environment information is image information of a real scene around a user, and the user wears a mixed reality display device; then, on the basis of the user environment information and the user motion information, generating a virtual image by using a three-dimensional interpolation volume rendering method; next, by using an optical model, projecting the virtual image into the visual field of the user, and merging the virtual image with the real scene to obtain a mixed reality rendered three-dimensional model; and finally, on the basis of the mixed reality rendered three-dimensional model, capturing a gesture of the user by using a gesture recognition method, and performing mid-air gesture interaction. The present application achieves mid-air gesture interaction between a user and a virtual scene by means of efficient three-dimensional object volume rendering and a real-time accurate interaction method design.

The invention discloses a geophysical three-dimensional visualization and geologic model construction system, which belongs to the technical field of geophysical data processing and three-dimensional geologic modeling and comprises a multi-source data acquisition module, a volume rendering module, an interactive segmentation module, an uncertainty expression module, a multi-scale fusion module, an attribute interpolation module and a model construction module. Multi-source geophysical data are converted into a visual three-dimensional visual model through a volume rendering technology, an interactive segmentation algorithm is adopted to accurately extract a geologic body boundary, an uncertainty expression mechanism is innovatively introduced to quantify the reliability of the model, and a multi-scale fusion technology is integrated to realize seamless connection from a regional structure to an ore deposit fine structure. The Kriging interpolation functional product physical property parameter three-dimensional distribution field is provided, the spatial cognitive ability and the decision-making efficiency under the complex geological condition are remarkably improved, and a powerful visual analysis platform is provided for geological exploration and resource evaluation.

Computing an output image of a dynamic scene. A value of E is selected which is a parameter describing desired dynamic content of the scene in the output image. Using selected intrinsic camera parameters and a selected viewpoint, for individual pixels of the output image to be generated, the method computes a ray that goes from a virtual camera through the pixel into the dynamic scene. For individual ones of the rays, sample at least one point along the ray. For individual ones of the sampled points, a viewing direction being a direction of the corresponding ray, and E, query a machine learning model to produce colour and opacity values at the sampled point with the dynamic content of the scene as specified by E. For individual ones of the rays, apply a volume rendering method to the colour and opacity values computed along that ray, to produce a pixel value of the output image.

The embodiment of the invention provides a three-dimensional virtual human video synthesis method, system and device and a storage medium, and the method comprises the steps: employing a unified camera position to separately shoot a site video and a field video, carrying out the detection of a person in each frame of image of the field video, and generating a pixel-level mask of each human body; on the basis of a parameterized SMPL human body model, registering human body contours formed by masks in each frame of mask grey map to obtain SMPL parameters of each registered human body; and taking the registered SMPL parameters of each human body as a new view angle of the implicit neural radiation field, synthesizing a video picture under the new view angle under volume rendering, and fusing the video picture with a three-dimensional virtual human to generate a video. The problem that in an existing method, registration optimization of a parameterized three-dimensional human body and the contour of human body segmentation is difficult to achieve is avoided, and the visual defect that in an existing image synthesis method, the posture of the virtual human in a video picture is adjusted only based on the human body posture, and consequently the virtual human foot suspension or foot sliding animation is likely to be generated can be avoided.

The invention discloses a blurred image NeRF modeling method, system and device based on a scattering light path model and a medium. The modeling method comprises the steps of blurred image initial pose calculation, camera pose interpolation, neural radiation field NeRF-based 3D scene modeling, scattering light parameter learning guided by an internal scattering light path model, blurred image synthesis prediction by using a scattering sensing volume rendering method, and parameter joint optimization based on blurred image luminosity loss. The system, the equipment and the medium are used for implementing the method. A light propagation phenomenon is represented as internal scattering of light at a medium or surface intersection point through an internal scattering light path model, a scattering light path direction and a sampling point distance are autonomously learned by means of a feedforward neural network, and image rendering is performed by integrating contributions of sampling points in the directions of a main light path and the scattering light path. The real imaging process of a blurred image in a complex illumination environment can be effectively simulated, the geometric ambiguity problem is effectively avoided, the convergence stability of the neural network is improved, and a finer reduction effect on geometric details of a fine object is achieved.

The invention discloses a space target three-dimensional reconstruction realization method based on radiation field generation. The method comprises the following steps: acquiring image data of a space target for preprocessing; an improved generated radiation field network is built, and an image is sampled to obtain an image patch; obtaining a group of three-dimensional sampling points in a layered sampling mode, performing multi-frequency position coding, splicing with a shape vector, sending into a shape coding layer to obtain feature representation, and inputting into a color head together with a sight direction code and an appearance variable to calculate the color of the sampling points; for each ray, colors of all sampling points are synthesized into a final pixel color based on a volume rendering equation, and the final pixel color is combined into a patch image to be output; inputting the real patch and the patch image into a discriminator, and calculating confrontation loss and discrimination loss; and acting the weight obtained by training on a generator to obtain rotating videos of the space target at different visual angles. The method can realize three-dimensional reconstruction of a space target, and has the advantages of flexible data driving, cross-view geometric consistency, efficient training and the like.

The invention belongs to the technical field of high-power microwave effect tests in laboratories, and relates to a lossless reinjection data synchronization method and device for a multi-mode test body, and the method comprises the steps: S1, data collection and transmission: collecting electric field data in the multi-mode test body, and transmitting the electric field data out of a shielding cabin of the multi-mode test body; s2, an edge processing step: sending the electric field data transmitted to the outside of the shielding cabin into an edge GPU node, and carrying out voxelization processing and WebGL body rendering; s3, a data synchronization step: synchronizing the three-dimensional rendering data stream to a plurality of user side browsers in real time through WebRTC and HTTPS (Hyper Text Transfer Protocol over Secure Socket) protocols; s4, time synchronization is carried out, a unified high-precision time reference is provided through a Whit-Rabbit clock, and sub-nanosecond alignment of multi-channel data is achieved; and S5, a data storage step: generating a hash abstract for the synchronous data and writing the hash abstract into the block chain. By means of the method, the safety, the real-time performance and the reliability of data transmission can be effectively improved.

The invention provides a spatio-temporal data volume rendering method oriented to an unreal engine, and belongs to the technical field of spatio-temporal data volume rendering. Ocean spatio-temporal data in a NetCDF format is acquired, a two-dimensional volume texture is generated, and a video memory partition management system is established to divide a GPU video memory into an upper layer video memory grid and a lower layer video memory grid; a double-layer game algorithm is adopted to realize differentiated resource allocation, a volume rendering material object is created in an unreal engine, a volume coloring framework based on light stepping is established, and the sampling step length is dynamically adjusted through an adaptive light stepping algorithm, so that efficient volume texture sampling based on a video memory grid is realized. And executing ray casting calculation to generate a three-dimensional visualization effect, establishing dynamic interaction control of space-time dimension to realize synchronous display of multi-source ocean data, and solving the technical problem of low rendering efficiency caused by unreasonable video memory resource allocation in the ocean space-time data three-dimensional visualization rendering process.