2111 results about "Augmented reality" patented technology

Automatic generation of in-store product information and navigation guidance, using Augmented Reality (AR) and a Vision-and-Language Model (VLM) and multi-modal Artificial Intelligence (AI). An automated method includes: providing to the VLM images that are captured within a retailer venue by an electronic device that is a smartphone or an AR device or smart glasses; (b) automatically feeding into the VLM those images, or pre-sliced or pre-cropped image-portions of those images that were sliced or cropped using Machine Learning that performs object boundaries detection and not product recognition; (c) invoking the VLM to generate outputs of VLM analysis of content of those fed images or image-portions. The VLM outputs can be: VLM-based product recognition, VLM-based product-related information, VLM-generated virtual shopping assistance, VLM-generated in-store navigation guidance, or other VLM-generated outputs. Based on the VLM-generated outputs, the electronic device provides real-time information about products depicted in the images, VLM-generated shopping assistance, and VLM-generated in-store navigation guidance.

The invention relates to the technical field of building information models, augmented reality, synchronous localization and map construction, and provides a deep learning-based SLAM-BIM augmented reality cooperative localization method and system, and the method comprises the steps: introducing a Transform time sequence feature extractor and a geometric relation graph, evaluating a dynamic distribution weight through combining with the confidence, achieving the cross-modal closed-loop detection, and obtaining an SLAM-BIM augmented reality cooperative localization result. A lightweight semantic segmentation network and a feature fusion module are utilized, a dense map with consistent geometric semantics is constructed, a space-time error propagation equation is constructed, online calibration is realized by means of BIM scale prior, an incremental fusion algorithm is designed, a global pose is optimized in combination with AR interaction, and the system fuses SLAM visual trajectory features and BIM semantic geometric features through a deep learning technology. According to the method, the problems that traditional SLAM accumulative errors are large and the BIM fusion precision is low are solved, robust positioning and map construction in a complex scene are achieved, the cooperation precision and real-time performance of SLAM and BIM are improved, and the method is suitable for AR scenes such as building construction and operation and maintenance.

The invention discloses a full-scene high-place operation intelligent monitoring system, and the system comprises the following steps: collecting environment, personnel health, equipment operation and operation path data in real time through a plurality of sensors, generating a safety ecological map through a data fusion technology, and reflecting the safety situation of an operation site in real time; an intelligent algorithm is adopted to analyze the data, safety risks are predicted, emergency early warning is generated, the operation task sequence is dynamically adjusted according to task priorities, risk levels and personnel health conditions, and resource allocation is optimized; in the operation process, the system provides real-time safety prompts and operation guidance for operators through virtual reality and augmented reality technologies, and safety awareness and decision-making ability are improved; the system has an automatic emergency response function, can optimize an evacuation route in real time, guarantees the safety of operators, can continuously optimize a safety management strategy through adaptive learning, and improves the operation efficiency and safety. The invention aims to improve safety and efficiency in high-risk working environments.

The invention relates to the technical field of product quality inspection, in particular to a product quality detection system based on deep learning and machine vision, which comprises a multi-modal image acquisition module, a deep learning defect detection module and a dynamic resource scheduling module. The multi-modal image acquisition module is used for acquiring product images in all directions by using a high-resolution camera array and a self-adaptive light source; the deep learning defect detection module accurately identifies defects through dual-path residual denoising, multi-scale context sensing and graph topology defect classification; the dynamic resource scheduling module realizes reasonable scheduling of personnel and materials and balance of a production line according to a detection result, a skill map, an ant colony algorithm and the like; the augmented reality interaction module visually displays defects and provides maintenance navigation through holographic projection and multi-mode interaction; the self-evolution detection model can update the weight based on a new sample, and the multi-physics field simulation module simulates the influence of defects on the mechanical property of a product. The detection precision is high, the efficiency is high, and the product quality detection and production management level is remarkably improved.

The invention relates to the technical field of highway engineering informatization, in particular to a geometric registration method and system based on a BIM model and an AR real scene. The method comprises the following steps: obtaining multi-source data of a construction site, and generating an AR real scene of the construction site according to the multi-source data; fusing multi-source data to obtain accurate parameters of the construction equipment; calculating a spatial transformation matrix of a construction site through accurate parameters of construction equipment, and obtaining an initial registration result of the BIM model and the AR real scene by combining engineering characteristics and the spatial transformation matrix; a dynamic error compensation mechanism is introduced, the initial registration result is corrected through the dynamic error compensation mechanism, and a local registration result is obtained; and constructing a global and local coordinate system mapping optimization model, and mapping the local registration result to a global coordinate system by using the global and local coordinate system mapping optimization model to obtain a global registration result. According to the method, a reliable spatial data basis is provided for highway engineering quality inspection, construction guidance and defect identification.

The invention discloses a community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

An aero-engine augmented reality virtual-real fusion method based on depth prior scene reconstruction comprises the following steps: firstly, collecting data of an aero-engine look-around RGB image and a depth image, calculating a camera frame pose by using an SFM algorithm and obtaining a sparse point cloud, and optimizing the camera pose by using an ICP algorithm and the point cloud converted from the depth image to obtain an aligned prior dense point cloud; gaussian points are initialized based on the priori dense point cloud and the sparse point cloud, and random generation of 3D Gaussian is limited by using the boundary of the dense point cloud as geometric constraint; through the three-dimensional reconstruction of the adjacent multi-view enhancement strategy, the spatial and visual consistency is improved, and an aero-engine model is reconstructed; then understanding the reconstructed aero-engine model based on semantic segmentation, identifying and marking core components, and finally performing virtual-real fusion visualization on the reconstructed aero-engine through augmented reality to guide training, assembling and maintenance operations. According to the invention, rapid high-quality three-dimensional reconstruction and augmented reality virtual-real fusion visualization of the aero-engine are realized.

The invention relates to the technical field of environment detection, and discloses a GIS map-based multi-class environment parameter automatic prediction method, which comprises the following steps: collecting noise intensity, vibration spectrum, sewage turbidity, illumination intensity and PM2.5 concentration in real time through a distributed sensor network, combining social behavior data, carrying out space-time alignment, and generating a multi-dimensional space-time matrix; calculating an energy overlapping degree by adopting a space-time diagram attention network, marking a high-risk collaborative pollution area, and predicting a pollution diffusion path; constructing a self-adaptive prediction model containing physical and behavior driving channels, and dynamically adjusting the weight to optimize the prediction precision; based on the optimization model, reversely deducing a pollution source of an overproof area, matching equipment characteristics and generating a control instruction; and opening an AR (Augmented Reality) interface to verify the treatment effect, and when the virtual-real data deviation exceeds a data deviation threshold, triggering federal learning to update the global model, and generating an environmental protection compliance report. According to the invention, the accurate decision-making efficiency of environmental governance can be improved.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

Provided are a processing system for a three-dimensional model, a method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system, a system for generating a three-dimensional model from a virtual reality / augmented reality three-dimensional sketch, a diffusion model training method, and a method for editing a three-dimensional model on the basis of a virtual reality / augmented reality three-dimensional sketch. The method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system comprises: step one, at an input apparatus, a user using a hand or a handle to draw three-dimensional sketch content in the air, wherein a three-dimensional sketch is stored in the form of a three-dimensional point cloud, and when the user draws a trajectory, a corresponding point cloud is generated in the drawn trajectory and is displayed in a three-dimensional virtual space in a highlighted manner, and displacement and deletion operations of the user for the three-dimensional sketch are overall displacement and deletion operations for points in a region corresponding to the point cloud of the three-dimensional sketch; step two, a computing apparatus converting the three-dimensional sketch content into a three-dimensional model; step three, converting the three-dimensional model into a readable file for an execution apparatus, wherein the conversion is implemented by using a slicing algorithm; and step four, a three-dimensional printer executing the readable file to perform a printing operation, so as to obtain a physical model.

The invention discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

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.

Camera and / or other sensor analysis of skin, used to diagnose and monitor health conditions. A computer, receiving skin data from a user, where the skin data is images and / or other data of the skin. The computer processes the skin data identify skin conditions, their status, and to compare the skin data with information indicative of health conditions that are not skin diseases. This can be done by finding similar skin data to other data received from other people who have specified health conditions and / or by looking for changes from previous images. The skin data can be captured by a smart phone camera (alone or with digital peripheral attachments) or a dedicated capture device such as a periscope, one way mirror, or cubicle. An augmented reality marking system can be used to mark areas on the user's skin to compare against other previous skin images and / or data.

The invention discloses an intelligent auxiliary acupuncture system based on the AR technology, and the system comprises the following modules: a multi-modal sensing module which is used for collecting a multi-modal sensing data set containing the surface information of a patient; the visual SLAM guiding module is used for outputting a relative pose between the camera and the skin of the patient; the three-dimensional reconstruction module is used for generating a three-dimensional skin grid model; the candidate acupoint generation module is used for generating a candidate acupoint set under the condition that the density threshold value and the minimum spacing are met; the stable matching module is used for outputting a stable matching result set; the trajectory planning module is used for binding the needle insertion trajectory to the three-dimensional skin grid model to form a needle insertion guide set; and the augmented reality rendering module is used for carrying out real-time visual rendering on the virtual acupuncture point target and the needle inserting track through an augmented reality display terminal. The invention constructs an intelligent acupuncture guide system which is oriented to a real clinical scene and has a real-time correction capability and a medical significance interpretation capability.

A computer-implemented system and method for pupil-directed retinal projection in near-eye displays are disclosed. The computer-implemented system provides smart glasses with directed physical pixels that project light beams / signals directly onto a user's retina based on pupil position and size. The glasses comprise a frame, lenses with directed pixel layers, and sensors for tracking pupil movement. Each directed pixel may generate multiple virtual pixels by rapidly changing its emission angle. The glasses function as prescription lenses, virtual reality displays, and augmented reality devices without traditional optical systems. Additional features include depth sensors, cameras, and connectivity to peripheral devices. The glasses enable a seamless blend of virtual and real-world experiences, creating an immersive “Mixverse” environment. Various input methods, including gesture recognition and brain-computer interfaces, allow for intuitive control and interaction.

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 relates to the technical field of fire fighting systems, in particular to an intelligent cockpit system for fire fighting, which comprises a perception analysis layer, a decision processing layer, a command execution layer and a reinforcement learning closed-loop architecture for mixed reward shaping, and integrates video streams, audio communication and firefighter physiological data containing heart rate variability through a deep multi-modal fusion module. Generating a global fire scene situation of physical constraint verification; through a risk sensitive type three-dimensional fire scene deduction module, a fire extinguishing strategy considering efficiency and safety is generated; through an immersive augmented reality visual interface, in combination with a synchronous positioning and mapping technology, precise navigation in a complex environment and superposed display of situation information containing risk levels are realized; a Q learning algorithm continuous optimization strategy including domain knowledge intermediate process rewards is adopted, closed-loop optimization of perception-analysis-decision-execution-feedback is achieved, and therefore the overall efficiency of fire rescue operation in modern complex disaster scenes is remarkably improved.

The invention discloses a civil engineering cost evaluation method and system based on model optimization, and particularly relates to the technical field of civil engineering cost evaluation. Three-dimensional building information and the actual environment of a construction site are combined by establishing a virtual simulation scene based on a BIM model and combining a virtual reality or augmented reality technology, meanwhile, environment variables related to actual construction are dynamically introduced, the variables are analyzed through a machine learning algorithm, risk assessment is conducted on different construction stages, and the risk assessment efficiency is improved. The BIM model is dynamically adjusted and optimized, and based on the optimized BIM model and the risk assessment result, cost assessment is performed for different construction schemes and a report is output, so that the difference between a traditional virtual environment and a real construction condition is effectively made up, the construction cost and risk can be accurately predicted, project hyperbranched and delay are reduced, and the construction efficiency is improved. And more accurate cost evaluation and construction management are realized.

The invention relates to the technical field of augmented reality and industrial safety monitoring, in particular to an AR glasses dangerous state dynamic labeling method and system based on multi-source perception, and the method comprises the steps: synchronously collecting data such as a depth image and gas concentration through multi-source equipment such as a depth camera and a gas sensor; extracting rotation invariant point cloud features through an improved dangerous target detection model, and fusing multi-modal data to generate a dynamic danger level; based on VIO, realizing accurate spatial registration of annotation and a real scene, and generating a self-adaptive annotation style according to a danger level; and multi-device labeling pose synchronization is realized through an entropy weighting ICP algorithm, and labeling transparency and size are dynamically optimized in combination with a user fatigue value and environment illumination. According to the method, the comprehensiveness of danger identification and the accuracy of marking are improved, team collaborative operation is supported, the method is suitable for scenes such as electric power inspection and dangerous chemical transportation, and the operation safety and efficiency are guaranteed.

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 discloses an artificial intelligence assisted makeup and makeup robot collaborative makeup method, and belongs to the technical field of artificial intelligence and robots. The method comprises the following steps: collecting user face data through a multi-modal sensor, extracting multi-scale features based on improved DenseNet and HRNet networks, and generating a three-dimensional face model; a high-fidelity virtual makeup effect is generated by using a conditional generative adversarial network in combination with user requirements, and real-time comparison and display are performed through an augmented reality interface; the makeup difference is quantified through multi-dimensional feature point mapping, multi-color space analysis and texture comparison, and correction suggestions are dynamically generated; operation is executed through the seven-degree-of-freedom redundant mechanical arm, a hybrid force control strategy and a visual monitoring system are integrated, and safe and accurate execution is ensured. The method solves the problems of uncontrollable traditional makeup effect, low execution precision and lack of dynamic feedback, has the characteristics of high adaptability, real-time response and zero-damage safety, and can be widely applied to the fields of family beauty, film and television makeup and special crowd nursing.

The invention discloses an efficient three-dimensional reconstruction method based on data-driven compression and adaptive splitting. The method comprises the following steps: acquiring a multi-view image to generate a 3D Gaussian; constructing a mask generation network and a discrimination network, generating a mask by the mask generation network, and screening the eliminated 3D gauss; the attributes of 3D Gaussian are optimized through a microgradable grating renderer; gaussian selection and elimination are carried out by using the mask, and a rendering image after partial Gaussian elimination is generated; if the preset number of iterations is reached, the contribution to the scene is reserved or eliminated according to 3D Gaussian; otherwise, performing Gaussian splitting or copying operation. And through antagonistic learning and reconstruction loss optimization of the mask generation network and the discrimination network, high-precision three-dimensional scene reconstruction is realized while the Gaussian number is reduced. The method is suitable for various three-dimensional reconstruction scenes, can greatly improve the rendering speed, reduces the consumption of computing resources, maintains the visual quality of a reconstruction result, and is suitable for the fields of virtual reality, augmented reality, movie special effects and the like.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention relates to the technical field of bridge monitoring, in particular to a bridge crack real-time monitoring method based on edge calculation, which comprises the following steps of: acquiring bridge crack data by using a hyperspectral imaging camera, a thermal imaging device and a laser radar, adaptively adjusting edge calculation nodes by using an edge learning technology, and calculating the edge calculation nodes according to the edge learning technology. A deep learning model is used for crack analysis, an anomaly monitoring model is established, an augmented reality technology is introduced to assist field investigation, and through comprehensive application of technologies such as multi-source data acquisition, edge calculation, deep learning and augmented reality, the problems that a traditional bridge crack monitoring method is low in efficiency, low in precision, incapable of real-time monitoring and the like are solved. The system has the advantages of comprehensive data acquisition, real-time and efficient processing, strong adaptive ability, comprehensive abnormity monitoring, high system reliability and visual and efficient field investigation.

In some embodiments, a computer-implemented method of accurately reproducing a color of a mixed product is provided. A color management computing system receives one or more inputs indicative of a target color. The color management computing system transmits a command to a product mixing device to dispense color component products to be mixed to create a mixed product of a first color. The color management computing system obtains an image that includes the mixed product. The color management computing system updates a display color correction profile based on a comparison of a captured color of the mixed product in the image to the target color. The color management computing system presents one or more instances on a display device of an adjusted target color indicative of the updated display color correction profile, in response to receiving a subsequent request to change the target color.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program, method, and user interface to facilitate context based augmented reality communication between multiple users over a network. Virtual content item configuration data indicative of a selection by a first user of virtual content item to apply to a real-world environment that is visible to a second user via a second device is received from a first device. The virtual content item configuration data also includes one or more criteria to trigger application of the virtual content item to the real-world environment. A triggering event is detected based on satisfaction of the one or more criteria determined from context data generated at the second device. The second device presents the virtual content item overlaid on the real-world environment that is visible to the second user based on the triggering event.

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 relates to the technical field of intelligent inspection, and discloses an intelligent inspection method and system based on AI multi-source fusion, and the method comprises the steps: collecting multi-mode perception data when an expert carries out the touch inspection operation; analyzing the multi-modal perception data, and constructing a biomechanical model and a tactile knowledge mapping model; establishing a standard tactile operation model and an acoustic feature mapping relation database; converting the standard tactile operation model into animation guidance and tactile feedback in an augmented reality interface; actual operation data of the inspectors are collected and compared with the standard touch operation model, and operation improvement suggestions are generated; fusing the tactile inspection result, the acoustic detection result and the visual inspection result to generate a comprehensive evaluation report; according to the invention, digital inheritance and standardized application of the tactile skills are realized, and the technical problem that the tactile skills are difficult to transmit in traditional inspection is solved.