1319 results about "Radiation field" patented technology

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

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

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

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.

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

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

The invention provides an autonomous navigation method and related equipment based on laser and vision fusion, and the method comprises the following steps: carrying out multi-sensor space-time joint synchronization processing on laser point cloud data, binocular image data and a clock signal, and generating a space-time aligned multi-modal data stream; the multi-modal data stream is processed through cross-modal feature pyramid and attention guidance fusion, and dense environment characterization and a dynamic object space-time mask are generated; determining a multi-layer semantic map corresponding to the dense environment representation through layered factor graph optimization; fusing the multilayer semantic map with the neural radiation field to generate a mixed map of an explicit semantic map and an implicit radiation field; and determining a motion track of the target equipment through space-time joint constraint on the mixed map and the space-time mask of the dynamic object. Through the implementation of the scheme of the invention, the real-time distinguishing between the dynamic obstacle and the static structure can be realized in a dynamic complex environment, so that the obstacle avoidance robustness of the motion trail is remarkably improved.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

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

The invention relates to the technical field of intelligent evaluation and early warning methods, in particular to a nuclear radiation dose space distribution intelligent evaluation and early warning method and system, and the method comprises the following steps: obtaining multi-source monitoring data which comprises gamma radiation dose, alpha radiation dose and beta radiation dose measuring instrument data; performing fusion and enhancement processing on the multi-source monitoring data to obtain an enhanced radiation monitoring signal; based on the enhanced radiation monitoring signal, high-precision prediction of nuclear radiation dose spatial distribution is carried out through a deep probabilistic neural network model; according to the high-precision prediction result, in combination with a nuclear accident severity level standard, a radiation alert evaluation result and early warning information are generated, short-term radiation fluctuation can be captured, a long-term change trend can be identified, and the understanding ability of the system to a complex radiation field is greatly improved.

The invention discloses a generalized neural radiation field reconstruction method based on multi-modal information fusion, and the method specifically comprises the following steps: 1, constructing a luminosity feature body and a geometric feature body based on unstructured multiple views, and constructing a multi-modal neural coding body through progressive complementary fusion; step 2, converting the multi-modal neural coding body and the original RGB pixel body of the unstructured multi-view into volume density and radiation brightness; 3, sampling light rays, and fusing context features of the sampled light rays to obtain context features of the light rays; and step 4, decoding the volume density and the radiation brightness by using the light context features, rendering to generate a free view angle RGB-D image, and then guiding dense reconstruction of a low texture scene by combining photometric supervision and sparse geometric supervision. According to the method, the problem of shape radiation ambiguity can be solved, high-quality three-dimensional reconstruction and two-dimensional rendering are realized, and the surface reconstruction precision of the generalized neural radiation field is improved.

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

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

The invention discloses a sparse view angle complex outdoor scene three-dimensional reconstruction method, electronic equipment and a storage medium. The method comprises the steps of obtaining camera pose information and initial three-dimensional point cloud information with rich geometric priori through an initialization module; according to the camera pose information and the initial three-dimensional point cloud information, in combination with a self-attention mechanism, a first target image is generated through a new view angle enhancement module, and the first target image is used as a pseudo-supervision signal to supervise an original rendering image; according to the camera pose information and the initial three-dimensional point cloud information, a high-fidelity second target image with transient shielding removed is acquired through a shielding processing module; and optimizing the three-dimensional Gaussian radiation field through a course learning strategy according to the first target image and the second target image, and reconstructing to obtain a three-dimensional image of the complex outdoor scene. The embodiment of the invention can reproduce the landscape scene vividly, provides immersive roaming experience, can improve the reconstruction effect and visual consistency, and can be widely applied to the technical field of computers.

The invention discloses a neural radiation field trusted twin modeling method based on evidence fusion and a storage medium. The method comprises the following steps: acquiring multi-view image data; constructing a neural radiation field model based on evidence deep learning, and modeling the volume density, color and uncertainty of spatial points through normal inverse gamma distribution; training the model by adopting a weighted loss function with data uncertainty; in the training process, the sampling density of a high-uncertainty region is increased through a self-adaptive resampling strategy, and the rendering quality is improved; applying the trained model to a defect sample test data set; and generating a three-dimensional reconstruction image and an uncertainty quantization graph through the model, performing credibility evaluation on the defect area, and generating a final detection result in combination with a rendered image and the uncertainty quantization graph. According to the method, a fusion method of evidence deep learning and a neural radiation field is introduced, so that high-precision and high-reliability sample supplement work of product surface defect detection is realized, and the reliability and robustness can be improved.

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

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

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

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

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

The invention relates to the technical field of agricultural informatization, in particular to an orchard three-dimensional reconstruction and fruit tree semantic segmentation method based on a neural radiation field, and the method comprises the following steps: carrying out the multi-view shooting of an orchard through an unmanned plane, and obtaining an original image data set for three-dimensional reconstruction; and preprocessing the original image data set, including camera pose estimation and distortion removal, image down-sampling, monocular depth estimation and semantic annotation, and generating image data of RGB images, depth maps and semantic maps suitable for neural radiation field network training. According to the method, under the condition of processing a large-scale outdoor environment, especially complex illumination conditions and variable terrains, through optimized unmanned aerial vehicle multi-view reconstruction, an improved NeRF algorithm and an introduced semantic field, the limitation of a traditional method can be overcome when the method faces a large-scale orchard scene, and the method is suitable for large-scale orchard scenes. And faster, finer and more accurate three-dimensional reconstruction is realized.

The utility model relates to the technical field of electron beam transmission, in particular to a magnet system. Comprising a first shaping unit and a second shaping unit, and the first shaping unit comprises first quadrupole iron tightly attached to an outlet of the accelerating tube and first octupole iron arranged at the focus position of the first quadrupole iron; the second shaping unit comprises second quadrupole iron and second octupole iron, the second quadrupole iron and the first octupole iron meet a first preset distance, the second octupole iron is arranged at the focus position of the second quadrupole iron, and the second shaping unit and the radiation field area meet a second preset distance; the inner diameter of the first octupole iron and the inner diameter of the second octupole iron are both larger than or equal to the maximum size of an electron beam group. Therefore, the quadrupole iron and the octupole iron are utilized to form the shaping unit which is homogenized in one direction, so that the problems that in the prior art, in the electron beam transmission process, a large amount of dose is lost, the size of a radiation field is limited, and the radiation field cannot be adjusted at will are solved; therefore, the electron beam bunch generated by the linear accelerator can be expanded and homogenized in a short distance.

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

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

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

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

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

The invention relates to the field of bridge detection, in particular to a bridge structure detection method and system based on 3D Gaussian scattering. The method comprises the steps that 3D Gaussian scattering data and environment sensor data of a bridge structure are collected at the same time; based on a pre-trained deep learning model, carrying out dynamic fusion on the acquired 3D Gaussian scattering data and environmental sensor data; adjusting the weight of the 3D Gaussian scattering data in real time according to the environment sensor data, compensating the influence of environment factors on the scattering data, and obtaining a fused comprehensive data set; modeling the fused comprehensive data set by using a 3D Gaussian function to generate a Gaussian radiation field model; and the health condition of the bridge structure is intelligently detected, a detection result is obtained, potential damage is identified, and maintenance suggestions are provided. According to the invention, the bridge detection precision is improved, the interference of environmental factors is reduced, and dynamic monitoring and intelligent evaluation are realized.

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

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