257 results about "Volume rendering" patented technology

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

The invention discloses a 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.

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 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 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 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 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.

The invention discloses an automatic driving scene three-dimensional reconstruction method based on multiple view angles and continuous depth modeling. The method comprises the following steps: realizing high-precision and real-time three-dimensional scene modeling by designing an LMV-NeRF model; according to the method, multi-view image data and a continuous depth representation technology are fused, camera parameters are utilized to align feature maps, and global consistency is enhanced through a cross-view attention mechanism; generating a group of depth values by adopting a hierarchical sampling strategy, predicting color and volume density in combination with a volume rendering formula, and finally generating a target image and a depth map under a new view angle; and obtaining a data set of the multi-view image in the automatic driving scene to train the model, and selecting an optimal model to obtain the target image in the new view. By fusing multi-view image data and a continuous depth representation technology, high-precision and real-time three-dimensional reconstruction of the automatic driving scene is realized, and the method is particularly suitable for dynamic environment perception and decision support in the automatic driving scene.

The invention relates to a three-dimensional scene cross-domain migration method based on neural radiation field representation, computer equipment, a readable storage medium and a program product. The three-dimensional scene cross-domain migration method comprises the steps that a real image is acquired for three-dimensional scene reconstruction; inputting the three-dimensional scene information into a geometric module to extract content features; inputting the three-dimensional scene information into a source domain coloring module to extract source domain style features; performing volume rendering to obtain a first rendered image, training and optimizing the geometric module and the source domain coloring module until the expectation is met, and freezing the geometric module; inputting the three-dimensional scene information into a target domain coloring module to extract target domain style features; performing volume rendering to obtain a second rendered image; performing style migration on the real image to obtain a stylized image; comparing loss training to optimize a target domain coloring module, and freezing the target domain coloring module until expectation is met; and receiving a specified view angle direction, respectively outputting features by using a geometric module and a target domain coloring module, and obtaining a target domain image through volume rendering.

The invention provides a neural radiation field compression rendering method based on decomposition expression, and relates to the technical field of computer graphics, and the method comprises the steps: inputting a multi-view image and camera internal and external parameters, carrying out the ray sampling, generating a space sampling point, carrying out the mixed feature coding of the space sampling point, and obtaining a multi-view image; obtaining three-dimensional voxel features, aligned and fused three-plane features and position codes, and splicing the three features to form fused features; inputting the fusion features into a factorization neural BRDF rendering network to predict volume density, geometric latent features, material parameters and reflection features, and improving the view angle color under the compression condition through a BRDF modulator; a ray weight is predicted through a plane-ray combined modeling module, and a compressed neural radiation field model is obtained through combined optimization of miniaturized body rendering, weighted reconstruction loss and compression constraint and is used for target view angle image rendering; according to the method, the storage overhead of the neural radiation field model is reduced, and meanwhile, the synthesis quality and rendering consistency of the new view angle under different compression ratios are improved.

The invention relates to the technical field of occupancy network training, and discloses a self-supervised occupancy network training method and system based on 3D Gaussian splash rendering, and the method comprises the steps: fusing the 3D voxel features of a vehicle-mounted image with embedded features containing initial Gaussian attributes, obtaining the fused voxel features, and carrying out the decoding based on a Gaussian attribute decoder, obtaining a 3D Gaussian ellipsoid of each voxel grid; projecting the 3D Gaussian ellipsoid to a 2D image space by using a Gaussian splash rendering technology to obtain a 2D depth map; and generating a target view through linear sampling based on the 2D depth map and the vehicle-mounted image at the adjacent moment, and calculating luminosity reprojection loss based on the vehicle-mounted image and the target view to train the occupancy network. According to the method, the volume rendering calculation complexity and the reasoning time proportion are reduced, and the operation efficiency of the self-supervised occupation network is improved; and meanwhile, rendering can be carried out on the premise of not reducing the resolution of the depth map and supervising the quality, and the training effect and performance of the self-supervised occupancy network are improved.

There is provided a system and method for determining a three-dimensional model of an object using a projector and a light sensor arranged in a stereo configuration. The method including: receiving pixel intensity values captured by the light sensor for a plurality of images of a scene containing the object, wherein the images of the scene each capture different projection patterns emitted by the projector onto the scene; determining, using the pixel intensity values from a set of the plurality of images, correspondence between a projector plane and a camera plane to form a three-dimensional model of the object, the correspondence determined using a trained artificial neural network model that uses a combination of volume rendering and geometric representation; and outputting the three-dimensional model of the object.

The invention discloses an extensible reconstruction method for a super-large scene based on 3D (three-dimensional) Gaussian Splitting. The balance of the number of sub-block images is ensured through image perception self-adaptive blocking; a low-contribution view is removed through SSIM pruning; a priori depth is obtained based on virtual stereo rendering, a confidence mask is generated through multi-view verification, boundary consistency loss is constructed, and Gaussian densification is driven; and finally, carrying out TSDF fusion to obtain a seamless grid. Experiments show that reconstruction is completed in a 2.7 km scene within 6 h, MatrixCity-Aerial F1 reaches 0.784 and is improved by 41% compared with City GaussianV2, precision, efficiency and boundary consistency are considered, and the method is suitable for being applied to oversized scenes such as digital twin cities and automatic driving.

The invention provides a Bayesian nerve radiation field modeling method and system based on uncertainty perception and dynamic importance sampling. The method comprises the following steps: replacing a full connection layer in a multi-layer perceptron with a Bayesian linear layer to obtain a Bayesian neural radiation field BN-NeRF model; acquiring a data set containing house source photos of different viewing angles and corresponding camera positions, and training a BN-NeRF model by using the data set; performing preliminary coarse sampling on each light passing through the house source scene to obtain a coarse sampling point set, and performing uncertainty evaluation on each sampling point in the coarse sampling point set by adopting a trained BN-NeRF model; according to the uncertainty evaluation result corresponding to the preliminary coarse sampling, performing secondary sampling on each light passing through the housing resource scene to obtain a fine sampling point set; and integrating the coarse sampling point set and the fine sampling point set to generate a final sampling point set, calculating the color and volume density of each sampling point in the final sampling point set by adopting a trained BN-NeRF model so as to carry out volume rendering, and generating a final house viewing picture.

The invention discloses a gold mine prediction method based on deep ground drilling data, and relates to the field of mineral resource exploration. The problems that a traditional method depends on artificial experience, ignores a multi-source geological element space coupling relation and lacks three-dimensional probability expression, so that prediction precision is insufficient are solved. Carrying out weight distribution calculation on each key mineralization factor by adopting the coupling strength coefficient, and carrying out comprehensive processing on numerical values of the plurality of mineralization factors through a weighted fusion algorithm to obtain a comprehensive mineralization index distribution diagram layer; based on the numerical value distribution characteristics of the three-dimensional mineralization index field, establishing a mineralization probability prediction model by adopting a Bayesian network algorithm, and if the mineralization index of a certain spatial position is higher than a critical value, calculating a gold ore occurrence probability numerical value of the position; and converting the gold ore occurrence probability value into a probability distribution body under a space coordinate system through a three-dimensional modeling algorithm, and performing rendering processing on the probability distribution body by adopting a volume rendering technology to obtain a three-dimensional visual model of the gold ore occurrence probability.

The invention belongs to the field of computer graphics and computer vision, and provides an explicit volume rendering primitive densification method based on detail perception gradient, which is suitable for a three-dimensional reconstruction task under a sparse view angle or sparse point cloud condition. According to the method, a detail perception gradient index is introduced, the fitting capability of a current model to a fuzzy region in an image is dynamically evaluated, and whether point refinement operation is executed or not is judged according to the fitting capability. Compared with a traditional densification strategy depending on position gradient, the index provided by the invention can more accurately identify a detail missing region, and the rationality of density distribution is improved. Furthermore, according to the method, visual angle correlation and gradient normalization information are combined, a threshold function is set to split and reconstruct Gaussian primitives, and low-overhead and controllably-distributed point set enhancement is achieved. While the compactness of the whole model is kept, the reconstruction quality of a complex structure and an edge region is effectively improved, and the method can be widely applied to various application scenes such as real-time rendering, underwater perception and sparse reconstruction.

The invention relates to an air visibility intelligent monitoring system and method based on three-dimensional space visualization. The system comprises a data sensing module arranged in a target monitoring area; the data fusion processing module receives the multi-source environment sensing signal and generates a data signal through a spatial interpolation algorithm; the three-dimensional visualization engine module receives the three-dimensional visibility field data signal and generates a visualization scene signal based on a volume rendering technology; the intelligent analysis early warning module receives the three-dimensional visibility field data signal and the dynamic three-dimensional visibility visual scene signal, and generates an early warning information signal through an integrated space analysis and prediction algorithm; and the comprehensive management application module receives the visibility time-space evolution trend and early warning information signal and the dynamic three-dimensional visibility visual scene signal. The air visibility intelligent monitoring system and method based on three-dimensional space visualization can solve the problems that a traditional visibility monitoring method is insufficient in spatial representativeness, lack of three-dimensional visual presentation and difficult to achieve accurate early warning.

The invention relates to the technical field of rice phenotype extraction, and discloses a rice fine phenotype extraction method based on three-dimensional vision, a terminal and a storage medium. The method comprises the following steps: acquiring a multi-view image of a rice plant by using a depth camera, and performing camera attitude estimation on the multi-view enhanced image to obtain camera internal and external parameters corresponding to each view; constructing a neural radiation field network, training the neural radiation field network by taking the enhanced image, the contour mask and internal and external parameters of the camera as input, and learning color and density distribution of a scene by utilizing a volume rendering technology; extracting surface points based on a mixed threshold strategy by using the trained neural radiation field network, and generating a rice three-dimensional point cloud; inputting the rice three-dimensional point cloud into a point cloud instance segmentation network, and outputting an instance mask containing each organ of the rice and a segmentation result of a three-dimensional bounding box; and counting phenotypic parameters of the rice according to a segmentation result. According to the method, the fine instance segmentation of the high-density point cloud is realized while the reconstruction precision is ensured, and the phenotypic analysis of the spike fraction is supported.

The invention relates to the technical field of data three-dimensional visualization, and discloses a volume rendering method and system based on WRF output data, and the method comprises the steps: carrying out the interpolation processing of the WRF output data to improve the data resolution, and building a bounding box; the emergent point and the light direction when the light passes through the bounding box are solved, and the sampling step length is adjusted by using the light length and the resolution; at each sampling point, calculating a color value and an opacity value of the point by adopting a layered resampling method and applying different interpolation methods; and finally, calculating reflectivity and shadow parameters by combining the gray scale and gradient information of the sampling points so as to optimize a transmission function and calculate a final color value of the pixel point. The method provided by the invention can realize efficient and accurate meteorological data volume rendering, has relatively high rendering quality and performance optimization, is suitable for the fields of weather forecast, environmental perception and the like, and can provide powerful technical support for meteorological data analysis and environmental monitoring and prediction.

The invention provides a shadow perception satellite image nerve radiation field surface three-dimensional reconstruction method, which comprises the steps of optimizing an RPC model based on a multi-temporal satellite image, generating a sparse point cloud, and constructing a unified coordinate system through outer bounding sphere normalization; three-dimensional points are sampled in the pixel line-of-sight direction, and a training set is constructed; the SDF network predicts that spatial points have symbolic distance values, the color network integrates SDF features and sight line direction output colors, and the shadow color network combines the sun direction to generate shadow vectors; the light color is predicted through volume rendering integration, the shadow probability is calculated based on secondary ray casting in the sun direction, and a loss function is constructed in combination with an irradiance error to jointly optimize network parameters; and generating a three-dimensional grid by using sampling points of the trained SDF network in a unified coordinate system and adopting a Marching Cubes algorithm. According to the invention, the convergence stability of the neural radiation field model on satellite data and the success rate and precision of scene three-dimensional surface reconstruction can be improved.

The invention discloses a monocular image three-dimensional model reconstruction method based on a diffusion model and a neural radiation field. The monocular image three-dimensional model reconstruction method comprises the following steps: S1, constructing a Zero-1-to-3 diffusion model network; s2, constructing a neural radiation field network; s3, constructing a two-dimensional neural rendering module network; s4, using a pre-trained Zero-1-to-3 diffusion model, taking the monocular image as input, and obtaining a plurality of images at different angles through the diffusion model; s5, training the neural radiation field network, minimizing loss through back propagation and optimization, and obtaining a reconstruction result through volume rendering; s6, rendering the reconstructed image from a low-resolution image to a high-resolution image by using a two-dimensional neural rendering module network; and S7, taking the rendered multi-view image as the input of the neural radiation field network again, and obtaining a final reconstruction result. According to the method, the technical problem of performing three-dimensional reconstruction on the monocular image can be effectively solved, and the reconstruction quality is further improved while the reconstruction quality is ensured.