2100 results about "Gaussian" patented technology

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.

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 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.

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 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 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 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 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.

The invention discloses a large language model end-cloud collaborative inference system based on low-rank fine tuning, and belongs to the technical field of inference optimization of end-side cloud computing. Establishing an end-cloud collaborative reasoning architecture, and in an offline stage, performing parameter fine tuning on a large language model by a cloud side based on training data of different downstream tasks; in the online stage, user requests are classified through'variational auto-encoder-Gaussian mixture model 'clustering, whether a low-rank adapter matched with a current task exists in an end side cache is judged, and if yes, reasoning is executed on the end side; and otherwise, forwarding the task to the cloud side. After a plurality of user requests are processed by the architecture, historical user requests and cache states are analyzed based on a Mama model, and an end-side low-rank adapter library is dynamically updated. And monitoring end cloud load and reasoning delay in real time, and issuing the new adapter to the end side according to the task repetition rate increment. According to the method, dynamic balance of the system is realized, and high efficiency and adaptability of the system are ensured while calculation and storage overhead are reduced.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention discloses a scene rendering method and system based on three-dimensional Gaussian splashing. The method comprises the following steps: organizing and constructing an original 3D Gaussian set to obtain a spatial hierarchical structure; generating corresponding level details for the primitives in the spatial hierarchical structure to obtain a spatial hierarchical structure associated with the level details; traversing the spatial hierarchical structure associated with level details, and executing hierarchical view cone cutting and shielding elimination to obtain a visible node list; traversing the visible node list, calculating a level detail selection standard, and generating a level detail activity Gaussian set; performing optimization sorting on the level detail activity Gaussian set to obtain an activity primitive list; and performing tile-based rasterization on the active primitive list, and performing adaptive processing according to level details of the primitives to obtain a final color value of each pixel. According to the method, the rendering performance can be greatly improved, the occupation of a memory and a video memory is remarkably reduced, the rendering quality is improved, visual flaws are reduced, and the expandability of the 3DGS rendering method is enhanced.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention discloses a sparse view angle three-dimensional reconstruction method based on 3DGS. The method comprises the following steps: providing a Gaussian field initialization model; according to the method, the FPN, the 2D U-Net and the MLP are used for forming a Gaussian parameter initialization network, the network integrates high-level and low-level semantic information, and the understanding of the network on multi-scale features is enhanced. And then a complete Gaussian field model is obtained by combining initial point cloud coordinates generated by DUSt3R. And then performing projection and rasterization under different visual angles on the 3D Gaussian ball by taking three-dimensional Gaussian splashing as a main body frame of an algorithm, performing adaptive adjustment of cloning and pruning on the 3D Gaussian ball with relatively large gradient change, and outputting an output result which is optical information and depth information of each visual angle image after rendering. Then, in gradient back propagation, pixel value absolute value deviation between the RGB image obtained by rendering and the RGB true value is supervised through L1 norm loss; the scene rendering quality is high, calculation is simple, and engineering implementation is easy.

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

The invention discloses rendering equipment based on three-dimensional Gaussian sputtering. The hardware architecture includes a memory interface, a pre-processing engine, a cardinality ordering engine, a rasterization engine, and the like. The preprocessing engine converts the three-dimensional Gaussian point into a two-dimensional representation and generates a key value pair containing a tile identifier and depth information; the cardinal number sorting engine performs staged efficient sorting through a parallel processing mechanism and a BRAM alternate working mode; and the rasterization engine performs rendering processing by adopting a full-pipeline design and an early-stage stopping mechanism. According to the method, parallel execution of depth sequencing and rasterization is achieved, memory access and calculation redundancy are reduced through optimized data flow design, an efficient and low-power-consumption hardware acceleration solution is provided for three-dimensional Gaussian sputtering rendering, and the method is particularly suitable for application scenes such as virtual reality, games and scientific visualization requiring real-time rendering.

The invention belongs to the technical field of rainfall prediction, and discloses a short and temporary rainfall prediction method based on multi-model random scheduling integration, which develops a robust training and pushing framework based on a continuous rolling prediction strategy, and decomposes long-sequence prediction into manageable stages. According to the method, training is carried out through teacher forcing and planned sampling, error propagation is relieved, and the training process is stabilized. The invention further designs asymmetric encoder-decoders (DSE and AFD) that achieve lower FLOPs than competitive baselines under standardized assessment, where DSE selectively compresses significant features and AFD stepwise reconstructs details to mitigate excessive smoothing problems. Finally, an intensity weighted Gaussian KL divergence loss function is designed, and the key problem of data balance is solved by modeling and predicting on a distribution level and endowing a large weight to a meteorological important heavy rainfall event.

The invention relates to a method and system for measuring the three-dimensional morphology of a metal hydrophobic surface based on white light microscopic interference, and belongs to the field of precision measurement, and the method comprises the steps: firstly, measuring a sample, scanning the surface of the sample, and synchronously collecting a white light interference image sequence containing surface contour information; adopting a bicubic interpolation algorithm to carry out interpolation processing on the acquired white light interferogram group, and constructing a high-resolution interference image sequence; analyzing the time-frequency domain interference signal through fast Fourier transform, effectively inhibiting noise interference in combination with a filtering technology, and accurately extracting an envelope curve of the interference signal; and carrying out Gaussian fitting on the envelope peak by adopting a least square method and positioning a peak value coordinate, and finally reconstructing a high-precision three-dimensional surface topography. According to the method, through advantage complementation of the bicubic interpolation algorithm and the fast Fourier transform method, the transverse resolution is remarkably improved while the longitudinal resolution is kept, and an effective technical means is provided for accurate characterization of the three-dimensional morphology of the metal hydrophobic surface structure.

The invention discloses a roadway deformation monitoring device based on a millimeter wave radar. The roadway deformation monitoring device comprises a main shell, the millimeter wave radar, a laser sensor, a PCBA (Printed Circuit Board Assembly) and a bubble level, and an integrated multi-modal data fusion analysis module, a dynamic measurement range intelligent adjustment module, a real-time deformation prediction and control module and a self-calibration and fault diagnosis module. The multi-modal fusion system fuses radar and laser data through a Kalman filtering algorithm, and full-scale high-precision monitoring is realized by using median filtering, Gaussian fitting preprocessing and dynamic weight distribution; the dynamic range measurement module intelligently switches measurement and frequency sweeping ranges based on a decision tree algorithm according to mining progress, geological conditions and distance fluctuation coefficients; the real-time prediction module adopts an ARIMA model to predict the deformation in advance, and automatically adjusts the scanning angle and the acquisition frequency; the self-calibration module uses a high-precision reflecting plate to correct errors everyday; the system supports 5G communication and remote control, has the advantages of high precision, self-adaption and high reliability, and is suitable for long-term deformation monitoring of mine roadways.

The invention discloses a three-dimensional dynamic scene graph construction method based on 3D Gauss, and belongs to the field of computer body intelligence. The implementation method comprises the following steps of: realizing object perception and semantic feature extraction of open vocabularies by utilizing a visual basic model; a 3D Gaussian scene with high fidelity and continuous object semantics is constructed through multi-view multi-dimension optimization of 3D Gaussian representation; constructing a multi-level three-dimensional scene graph, extracting spatial levels and semantic relationships among objects by using 3D spatial positions and semantic tags of instance objects existing in a semantic Gaussian graph, and constructing a multi-level spatial semantic topology to accurately represent an environment layout; according to the method for realizing local updating for the Gaussian scene graph based on the environmental structural similarity, environmental change detection is carried out through real-time RGB-D observation and the structural similarity between high-quality rendering views of the Gaussian scene graph, and corresponding local updating is carried out by using rapid training and differentiable rendering of 3D Gaussian representation. And the capability of adapting to a complex dynamic environment of the 3D Gaussian scene graph is improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a 3D representation of a scene. In particular, one of the methods includes obtaining a plurality of training images of a scene; initializing a three-dimensional (3D) representation of the scene, the 3D representation comprising a set of Gaussian distributions; and generating a final 3D representation of the scene, comprising, at each of a plurality of update steps, updating, using rendered images rendered using the set of Gaussians and corresponding training images, the set of Gaussian distributions. As part of the updating, gradient-based pruning, semantically-aware optimization, or both can be performed.