2479 results about "Gaussian" patented technology

Disclosed in the present invention is a dynamic human modeling method based on three-dimensional Gaussians. In the present method, a multi-view human video is given firstly, and in order to correct and transform the dynamic human appearance, a latent code and a set of blend weights are added to each Gaussian; and the latent code and a target pose are inputted to an MLP to correct the Gaussian in a canonical space, so as to capture an appearance change under the target pose. The corrected Gaussian is transformed into the target pose by means of the blend weights thereof in a linear blend skinning (LBS) manner. Finally, a realistic human image at a new viewing angle or a new pose can be rendered in real time by means of Gaussian splatting. Compared with an existing method based on an implicit neural radiance field (NeRF), the three-dimensional Gaussian representation of the present invention can better capture high-frequency details while achieving optimal rendering performance.

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

The invention discloses a historical block scene three-dimensional reconstruction method and system based on Gaussian sputtering, and the method comprises the steps: collecting a historical block video sequence through a lightweight panorama camera, extracting a multi-frame panorama, and generating an image data set through a projection converter; monocular depth and normal estimation is carried out through a pre-training visual model, and a prior depth and normal graph data set of a historical block scene is constructed; sparse reconstruction is carried out on the multi-view image data set based on the SfM technology, initial point cloud and camera pose information are acquired, and a Gaussian ellipsoid is optimized in combination with prior depth and normal information; dynamically calculating the geometric width estimation value of the street, and guiding and adjusting the adaptive density of the Gaussian ellipsoids of the vertical surfaces on the two sides; rendering the optimized Gaussian ellipsoid through an improved rasterization renderer; and designing a multi-modal loss function and a regularization mechanism to optimize reconstruction and rendering results. According to the method, high-fidelity three-dimensional reconstruction of the historical block scene is realized, and the adaptability of the Gaussian sputtering method to the complex block scene is enhanced.

Aspects of the subject technology provide for human subject Gaussian splatting using machine learning. A method includes receiving a video input having a scene and a subject. The method also includes obtaining a three-dimensional (3D) reconstruction of the subject and the scene from the video input. The method includes generating a 3D Gaussian representation of each of the scene and the subject. The method also includes generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject to the 3D reconstruction of the subject. The method includes rendering a visual output comprising an animatable avatar of the subject and the scene using differentiable Gaussian rasterization based at least in part on the deformed 3D Gaussian representation of the subject and the 3D Gaussian representation of the scene.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition 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 discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

The invention discloses a personnel positioning method and system based on a 3D Gaussian splash model and video fusion. The method comprises the following steps: firstly, acquiring input data of at least two visual angles through a multi-visual angle video stream acquisition module, and constructing an initial three-dimensional Gaussian model by utilizing a sparse point cloud initialization module; a time sequence dynamic tracking module is combined with an optical flow algorithm to realize cross-frame parameter updating of a Gaussian ellipsoid, and a time sequence consistency optimization module is adopted to suppress parameter drift; identifying a constructor bounding box by using a target detection module, and establishing a corresponding relation between pixels and three-dimensional coordinates through a three-dimensional-two-dimensional space matching module; three-dimensional coordinates are calculated through a multi-view fusion positioning module, and the positioning precision is improved through a multi-sensor fusion optimization module in combination with IMU data. A 3D Gaussian splash model is combined with multi-view geometry and time sequence optimization, high-precision personnel dynamic positioning without marking in a construction scene is realized, and the problems of tracking drift and shielding in a complex environment in a traditional method are effectively solved.

The invention discloses an efficient 3D Gaussian scene reconstruction method based on multi-modal depth distribution supervision, and compared with the prior art, pixel-level depth dense point clouds are directly extracted in a Gaussian initialization stage, a multi-modal depth distribution map is generated, random initialization of dense Gaussian positions according to COLMAP sparse point clouds is effectively avoided, and the reconstruction efficiency is improved. The problems that under the condition of few views, point clouds obtained by COLMAP are too sparse, provided geometric constraints are insufficient, and meanwhile randomly initialized Gaussian blocks the point clouds to move to the correct position due to the local minimum value are solved. According to the method, an image segmentation algorithm based on edge detection is applied to the image frame, a large number of Gaussian primitives which are far away from edge points and are similar in depth are fused, the 3D Gaussian number is effectively pruned, and memory consumption of a GPU is saved. According to the method, extra isotropic regularization loss is introduced into global gauss, so that the problem of Gaussian distribution in which artifacts are generated along the line-of-sight direction and the height is lengthened is avoided.

The invention discloses a three-dimensional Gaussian sputtering method for sparse visual angle semantic priori. The method comprises the following steps of: 1, acquiring a target scene image, constructing a real image set as a data set, and manually selecting an interested object in a target scene to perform semantic three-dimensional reconstruction to obtain an initial semantic image of a sparse view angle; a multi-view image is collected, camera external parameters and scene sparse point clouds are obtained, a plurality of views are selected and input into the SAM2 segmentation model, and a view set Is with semantic images is obtained; 2, using a pre-trained SAM2 segmentation model as an interactive image sequence segmentation model, and initializing a three-dimensional Gaussian primitive according to the scene sparse point cloud; and step 3, training parameters of semantic three-dimensional Gaussian sputtering based on the trained three-dimensional Gaussian sputtering model and the interactive image sequence segmentation model, and reconstructing a target scene. According to the method, the high-quality semantic model is efficiently reconstructed. And the reconstruction result can be easily corrected through the interactive graphical interface in the training process.

A high-quality digital human avatar is quickly reconstructed from a monocular video and is rendered in real time, the method is widely applied to various fields, and an existing method based on a neural radiation field is high in training and rendering cost. According to the dynamic human body three-dimensional modeling method based on the three-dimensional Gaussian sputtering technology, three-dimensional Gaussian can be expanded to a dynamic human scene, and rapid training and real-time rendering can be achieved. Specifically, the three-dimensional Gaussian of the human body is initialized in a standard space, and the three-dimensional Gaussian is converted from the standard space to a posture space by utilizing linear hybrid skin (LBS) according to posture parameters, so that fine details of the human body are learned at very low calculation cost; a posture parameter refining module and an LBS weight optimization module are designed to improve the problem of inaccurate detection of posture parameters in a real data set, and multi-resolution hash coding is introduced to accelerate the model training speed.

A high-fidelity three-dimensional reconstruction method for a specular reflection plane comprises the steps of decomposing pixel colors into diffuse reflection and specular reflection components based on a 3D Gaussian sphere, introducing a dynamic reflection ratio parameter and a spherical harmonic function coefficient to respectively represent two reflection characteristics, and simulating light multi-reflection behaviors through weight fusion of cumulative projection and a reflection ratio map. Secondly, in combination with monocular inverse depth calibration, depth smoothing constraint of color gradient weighting and edge mutual exclusion loss, geometric consistency is enhanced, and artifacts are suppressed; a progressive multi-resolution training strategy is further adopted, low resolution is gradually optimized to complete resolution, reflection parameters are activated in stages, 3D Gaussian sphere overgrowth and floating artifacts are inhibited, and efficiency and precision are balanced. According to the method, the reconstruction fidelity of the specular reflection scene is remarkably improved while the real-time rendering advantage of the 3DGS is reserved, and the method is suitable for the high-precision modeling fields of virtual reality, augmented reality and the like.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

The invention discloses a reflecting object inverse rendering method, system and device based on two-dimensional Gaussian sputtering and multi-mode diffusion prior and a medium. The method comprises the steps that images and camera parameters of a reflecting object under multiple visual angles are acquired; calculating an observation angle according to the camera parameters; performing rasterization rendering on the images of the reflecting object under the multiple visual angles by using the trained two-dimensional Gaussian primitives under the observation visual angle to obtain a rendered image under the new visual angle; according to the method, two-dimensional Gaussian is adopted as a scene representation element, surface characteristics are better fitted, and geometric reconstruction precision can be remarkably improved; the trained two-dimensional Gaussian primitive is closer to a real three-dimensional scene, and experimental tests of multiple data sets show that the method not only can accurately reconstruct geometric and material distribution of a high-reflection area, but also can recover clear environment illumination with high-frequency details, realizes efficient and real reflection inverse rendering, and has a good application prospect. The method can be widely applied to downstream tasks such as three-dimensional reconstruction, material editing and relighting.

The invention discloses a seismic data constrained direct current method three-dimensional inversion method, which relates to the field of geophysical exploration and comprises the steps of processing seismic data and performing inversion processing on resistivity data. The seismic wave layer velocity information is used for constraining high-density electrical method inversion, so that a supplement effect to a certain degree can be achieved, three-dimensional seismic image information can be used for supplementing the resolution ratio of resistivity inversion, the boundary of an underground electrical anomalous body is reflected, and meanwhile, through the comprehensive geological-geophysical information, the electrical anomalous body can be accurately determined. And boundary features of the anomalous body can be further depicted. Seismic data constraints are applied to three-dimensional resistivity inversion, numerical simulation and inversion processing are performed on electrical method data under rugged topography according to finite volume method numerical simulation and a Gaussian-Newton inversion method, and meanwhile, seismic anisotropic velocity tensor constraints are introduced to perform inversion, so that the problem of multiplicity of solutions of inversion of a single method is reduced, and the inversion accuracy is improved. A more accurate three-dimensional underground electrical structure is obtained, and the resolution of inversion to a longitudinal boundary is improved.

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

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

The invention discloses an electric power scene defect small target detection method based on Gaussian mask supervision and cross-layer attention guidance, and the method comprises the steps: inputting an electric power scene image into a detection model, extracting an initial feature map through a backbone network, carrying out the multi-stage feature extraction of the initial feature map according to a convolution path, and carrying out the multi-stage feature extraction of the initial feature map; processing the multi-stage features based on a path aggregation network, and outputting a plurality of fusion feature maps with different feature levels from shallow to deep; and based on cross-scale window attention, guiding a shallow fusion feature map to carry out semantic information modeling by using a deep fusion feature map with high semantics in every two adjacent fusion feature maps, and after a plurality of output feature maps are obtained, respectively processing and outputting prediction results by using a multi-branch detection head. According to the method, shallow feature activation prediction and cross-scale window attention guidance are fused, and the detection robustness and positioning precision of a tiny fault target in an unmanned aerial vehicle inspection image can be effectively improved.

The embodiment of the invention provides a scene surface reconstruction method and device, equipment and a medium, and relates to the technical field of computer graphics. The method comprises the following steps of: firstly, introducing an initialized three-dimensional Gaussian model, and determining attribute information and position information of two-dimensional Gaussian corresponding to three-dimensional anchor Gaussian by combining a multi-view image sequence of a target scene; secondly, utilizing the attribute information and the position information to generate a rendered image through a Gaussian splashing method; and then, according to the rendered image and an image with the same view angle as the rendered image in the multi-view image sequence, updating the initialized three-dimensional Gaussian model by minimizing a rendering loss function. And finally, depth information is determined according to the updated three-dimensional Gaussian model and the multi-view image sequence, and a surface model of the target scene is constructed through a three-dimensional reconstruction algorithm according to the depth information and the rendered image, so that the structure and details of the target scene can be reflected more accurately, the floating object phenomenon is avoided, and the accuracy of the target scene is improved. And the quality and the accuracy of scene reconstruction are obviously improved.

The invention discloses a reinforcement learning adaptive multi-mode SLAM (Simultaneous Localization and Mapping) method based on 4D Gaussian splashing, and belongs to the field of simultaneous localization and map construction. An overall solution of further combining 4D GS and reinforcement learning on the basis of an existing multi-sensor fusion SLAM system is provided, a sensor mode is autonomously selected according to real-time environment information through reinforcement learning, and meanwhile, map updating and optimization are performed by utilizing the 4D GS and are integrated into the SLAM system. Therefore, the purposes of improving the working efficiency and the real-time performance of the whole real-time multi-mode SLAM system and saving resources are achieved. Comprising the following steps: step (1), multi-source sensor input and data preprocessing; (2) selecting a sensor combination type; (3) carrying out multi-source sensor data fusion processing; and step (4), updating and optimizing the map.

The invention provides a multi-modal gait recognition method and system, and aims to solve the problems of accuracy and robustness of gait recognition in a complex environment. According to the method, two types of modal information of a gait contour map and a skeleton Gaussian heat map are combined, global-local shallow gait features are extracted by using a multi-scale convolution feature extraction module based on residual connection, and adaptive weight distribution and fusion are performed on the features of the two types of modals through a cross-modal attention fusion module. Furthermore, a sliding window Transform module is introduced to perform deep gait feature extraction on the fused features, and a long-range space-time dependency relationship of gait information is modeled to obtain a gait feature vector with high resolution. Experimental results show that the gait recognition precision of the method in a complex scene is remarkably superior to that of an existing method, and particularly, the gait recognition precision is excellent under extreme conditions of shielding, complex backgrounds and the like. The invention provides a new solution for high-precision gait recognition in an open environment.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention discloses a hybrid Lp regularization magnetotelluric two-dimensional inversion method based on adaptive weight, and the method comprises the steps: reading magnetotelluric observation data, carrying out the quadrilateral finite element grid discretization of a whole inversion region, building an inversion grid, and building an initial model and a prior model of the inversion conductivity of the inversion region; based on the initial model and the prior model of the inversion conductivity and the magnetotelluric observation data, constructing a mixed Lp regularization inversion objective function comprising an L1 regularization item, an L2 regularization item and a data fitting item; in an inversion iteration process, adaptively adjusting weight factors of the L2 regularization item and the L1 regularization item; and based on the inversion grid, performing magnetotelluric two-dimensional inversion according to the inversion objective function mixed with Lp regularization, the initial model and magnetotelluric observation data, and outputting optimal model parameters of Gaussian Newton inversion to obtain underground electrical structure information. According to the invention, the resolution and accuracy of the inversion result are improved.

The invention provides a three-dimensional Gaussian digital human generation system and method and electronic equipment, and relates to the technical field of augmented reality, and the system comprises a data obtaining unit which is used for obtaining visual depth data, dynamic behavior data and environmental perception data, and generating point cloud data and multi-modal data according to the visual depth data, the dynamic behavior data and the environmental perception data; the rendering adjustment unit is used for monitoring the fixation area in real time and determining a rendering strategy; according to the real-time computing power parameter, determining a rendering specification; the driving unit is used for performing feature extraction on the multi-modal data to obtain multi-modal features and generating face driving parameters and limb driving parameters of the digital human model; and the modeling unit is used for generating a basic grid of the digital human model according to the point cloud data and the initial digital human model, and performing expression rendering and action rendering on the basic grid according to a rendering strategy and a rendering specification in combination with the face driving parameters and the limb driving parameters to obtain a rendered digital human model. The fluency, authenticity and naturalness of the display effect are improved.

The invention provides a dynamic scene reconstruction method and system based on spatial decomposition and Gaussian splashing, and belongs to the technical field of computer vision and graphics. The method comprises the following steps: decomposing a dynamic scene into a static standard space and a dynamic playground; reconstructing the standard space by using three-dimensional Gaussian splashing to obtain Gaussian primitives of the standard space; decomposing the characteristics of the motion field into standard space characteristics and a plurality of motion subspace characteristics based on tensor decomposition; fusing the standard space feature and the multiple motion subspace features through a motion information decoder, and decoding motion information of Gaussian primitives in the standard space; and applying the motion information to Gaussian primitives of a standard space, generating any moment representation of a dynamic scene, and rendering a target view angle image through a snowball throwing method to realize reconstruction of the dynamic scene. Through the method, the reconstruction precision and the rendering quality are effectively improved, and the real-time rendering capability is kept.

The invention provides a three-dimensional Gaussian splash reconstruction method and related equipment, and the method comprises the steps: carrying out the grouping processing of an obtained image sequence, obtaining a plurality of image pairs, enabling the image sequence to comprise a plurality of images with uncalibrated attitudes, and enabling any one of the plurality of image pairs to comprise two images in the image sequence; obtaining a global point cloud and a camera attitude based on the paired point diagrams determined by the plurality of image pairs and a confidence map associated with the paired point diagrams; and performing iterative training on parameters of the initialized three-dimensional Gaussian ellipsoid based on the camera attitude to obtain a three-dimensional Gaussian model, the initialized three-dimensional Gaussian ellipsoid being obtained by performing initialization processing on a preset three-dimensional Gaussian ellipsoid according to the global point cloud, and the three-dimensional Gaussian model being used for completing a new view angle synthesis task. The robustness of point cloud initialization is improved, it is ensured that the number of point diagrams is kept high in speed and memory no matter under the dense or sparse condition, and high-quality three-dimensional reconstruction and new view angle synthesis are achieved.

The invention relates to a low-orbit communication satellite Doppler positioning method and device based on factor graph optimization. The method comprises the following steps: after required observation data is received and an initial state value of a ground receiver is solved, a factor graph framework is introduced to set a priori factor, a Doppler frequency shift factor, a state constraint factor and a state variable consisting of a three-dimensional position and frequency drift of the receiver according to a Doppler positioning observation model; and constructing a Doppler positioning factor graph model by taking the factors and variables as nodes, introducing a sliding window mechanism to update a factor graph in a factor graph optimization solution process, and solving state variables by adopting a Gaussian-Newton algorithm to finally obtain a Doppler positioning result. According to the method, factor graph modeling is carried out on the Doppler positioning observation model, the advantages of nonlinear modeling, global optimization and capability of fully utilizing historical observation of a factor graph optimization method are exerted, and a relatively small positioning error and a higher convergence speed can be obtained.

The invention discloses a three-dimensional scene and object reconstruction method, system and device based on SDF and a Gaussian field, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem that the three-dimensional scene reconstruction quality is not high due to the fact that geometric precision and rendering quality cannot be achieved at the same time in the three-dimensional reconstruction process in the prior art. The method comprises the following steps: initializing and updating a TSDF voxel volume by using a depth map and an RGB image to obtain a camera external parameter and a scene three-dimensional initial model; sDF prediction is carried out on the scene three-dimensional initial model through an SDF neural network, scene surface points are generated according to the light direction, and a high-precision scene geometric model is obtained; training the SDF neural network; performing grid division on a bounding box of the scene three-dimensional initial model, outputting an SDF predicted value of each grid point by using the trained SDF neural network, and extracting a refined scene grid model; and performing Gaussian rendering by using the high-precision scene geometric model to generate a final rendered image.