10332results about "Image generation" patented technology

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.

Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate a three-dimensional voxel representation of a scene based, at least in part, upon a plurality of two-dimensional images of the scene.

The invention provides a construction scene prediction method and device fusing an image, a text and a BIM modal, and relates to the technical field of intelligent construction prediction management. According to the method, the BIM semantic graph is constructed by extracting the BIM semantic information of the BIM model, and the target detection recognition of the construction site video is carried out in combination with the YOLO model to obtain the target detection result; performing cross-modal alignment with the CLIP to realize deep fusion of the multi-modal data of the image, the text and the BIM to obtain a multi-modal heterogeneous graph; and inputting the multi-modal heterogeneous graph into a space-time sequence model for prediction, outputting prediction results of construction scenes at a plurality of moments in the future, and dynamically mapping the prediction results to a digital twinborn platform to realize risk early warning and visual display. The construction dynamic change can be captured in real time, the construction progress and risk can be accurately predicted, and the intelligent level of construction management is improved.

In various examples, systems and methods are disclosed relating to generation of four-dimensional (4D) content models, such as 4D content models to render realistic sequences of frames of 3D data. The systems can initialize a 3D component of the 4D content model, such as a 3D Gaussian splatting representation, based at least on a prompt for the 4D content. The system can configure motion and / or dynamics for the sequence of frames by evaluating frames rendered from the 4D content model using one or more latent diffusion models (LDMs), including a video LDM. The system can perform operations such as autoregressive generation of frames to create long sequences of content, motion amplification to facilitate realistic, dynamic motion generation, and regularization to facilitate generation of complex dynamics.

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.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

Methods for displaying and organizing user interfaces in a computer-generated environment provide for an efficient and intuitive user experience. In some embodiments, user interfaces can be grouped together into a container. In some embodiments, user interfaces can be added to a container, removed from a container, or moved from one location in the container to another location. In some embodiments, a visual indication is displayed before a user interface is added to a container. In some embodiments, a user interface can replace an existing user interface in a container. In some embodiments, when moving a user interface in a computer-generated environment, the transparency of a user interface that is obscured can be modified.

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

Disclosed are systems and methods for generating a 3D model from a single 2D image, the method comprising: receiving a single 2D input image of an object; generating a set of consistent multi-view images based on the single 2D input image using a fine-tuned 2D diffusion model that processes multiple views together in a tiled configuration; constructing a 3D feature volume by projecting 2D patch features from the generated multi-view images using corresponding camera pose information; generating a 3D mesh using a pair of 3D diffusion networks conditioned on the multi-view images, wherein the pair of 3D diffusion networks comprises a first network for generating a coarse occupancy volume and a second network for generating a high-resolution sparse volume; and refining a texture of the generated 3D mesh to produce a textured 3D mesh.

The invention relates to the technical field of control and regulation systems, in particular to a hearth auxiliary regulation control system which comprises a data processing module, an analysis module, a control module, a regulation and control module, an optimization module and a safety early warning module. The data processing module collects temperature data of a hearth combustor area, a water cooling wall and a tail flue in real time through a distributed temperature sensor network, and a dynamic combustion field three-dimensional model is generated through Kalman filtering denoising, redundancy cross validation and a temperature compensation algorithm in combination with combustor flame position signals and an air door opening state. And the optimization module updates the strategy weight based on the combustion efficiency improvement rate, and reinforces learning to extend the adaptive boundary of the strategy library. And the safety early warning module calculates the matching coefficient of the pipe wall temperature and the allowable stress in real time, triggers a grading intervention strategy to reduce the overtemperature risk, forms full-process closed-loop control, and improves the thermal efficiency and the operation safety of the boiler.

The invention discloses a digital twin three-dimensional scene modeling method based on a webGPU, and relates to the technical field of three-dimensional scene modeling and graphic computing, multi-source sensing data are asynchronously sampled from sparse point cloud, a video texture sequence, a scene semantic tag graph and a structure boundary tuple, and a six-dimensional structure unit group is generated through a normalization operator; constructing a structural unit atlas with nodes representing component entities and edges representing constraint relations, and introducing a tension balance mechanism and multi-scale constraints to generate a modeling path prior model; constructing a graph calculation and graph rendering dual-channel assembly line in the WebGPU, and executing parallel texture mapping and boundary fitting operation; a dynamic sensing module is used for capturing scene disturbance and driving atlas response, and incremental reconstruction of the model is achieved; and finally, mapping the model to a Web terminal, and supporting microscopic semantic query and multi-layer data linkage. According to the method, the response speed, semantic consistency and structural adaptability of three-dimensional modeling in a complex environment are improved.

Methods and systems are described for 3D modeling and visualization of a patient's facial structure and features for dental treatment planning.

The invention relates to a cloth defect detection and classification method for garment production, and the method comprises the following steps: carrying out the localization of a cloth image in the space, and constructing a dynamic response model based on the texture continuity of a local region; segmenting the continuous cloth image into sliding windows; constructing a local texture response sequence on a time axis for each window; a dynamic response index DRI is used for describing the stability of the area in space and time, and the stability serves as the basis of anomaly judgment; detecting the position and the change track of the texture response mutation, and reasoning the boundary and the form of the flaw; capturing jump points of which the variation amplitude is greater than a set threshold value in the response sequence; deducing a shape boundary and a diffusion path in the image in combination with a sudden change trend between adjacent windows; constructing a sudden change trajectory diagram to describe the real form of the flaw; judging whether the area belongs to a real defect or not by analyzing the structural stability of the defect boundary, and forming a defect credibility index DCI; and calculating the direction consistency and the edge gradient stability of the flaw boundary.

The invention discloses a digital twin substation multi-source data visualization decision-making method, which comprises the following steps: constructing a three-dimensional holographic digital twin model of substation equipment, integrating BIM data and GIS geographic information, and carrying out lightweight processing; multi-source heterogeneous data, including equipment state data, environment sensing data and video monitoring data, of the transformer substation are collected in real time. Through the technologies of multi-source data fusion, dynamic digital twin modeling, AI aid decision making and the like, holographic data integration is realized, data islands are broken, and unified analysis and visualization of multi-dimensional data such as equipment states, environments, videos and the like are realized. And real-time dynamic mapping is carried out: high-precision digital twin bodies are constructed, the operation state of a physical substation is synchronized, and the fault positioning and prediction capability is improved. And intelligent decision support: in combination with machine learning and an expert knowledge base, fault root cause analysis, risk assessment and optimization operation and maintenance schemes are provided, and manual intervention requirements are reduced.

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

The invention provides a large-scale scene multi-level-of-detail cloud rendering processing method and device based on 3DGS, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: dividing a target modeling scene into a plurality of sub-blocks; performing particle redundancy reconstruction and overlapping region marking on boundary regions between adjacent sub-blocks of each sub-block to obtain processed sub-blocks; performing multi-detail level division on each processing sub-block to generate a particle level set; determining a current visual area according to the user motion data, and scheduling a target hierarchy of a particle hierarchy set in the current visual area; the rendering tasks of all the processing sub-blocks of the target hierarchy are distributed to a plurality of rendering nodes to execute real-time rendering operation; and performing video stream coding on pictures rendered by each rendering node, decoding and displaying received video stream data, and performing particle level updating and re-rendering operation according to an interaction instruction. According to the invention, high-quality detail rendering can be realized for a model of a large scene.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

Systems and methods for four-dimensional analysis of a patient's coronary arteries and myocardial wall. An example method includes accessing angiographic x-ray images of one or more arteries, with the x-ray images depicting the arteries from respective angles, with each x-ray image being associated with a cardiac phase of cardiac phases, and with the cardiac phases being included in a cardiac cycle. A three-dimensional model of the arteries is generated for each cardiac phase based on a subset of the x-ray images which are associated with the cardiac phase. A four-dimensional representation of the arteries is generated throughout the cardiac cycle based on the three-dimensional models for the cardiac phase. Information associated with the four-dimensional presentation is presented.

The invention relates to the technical field of image processing, in particular to a glioma area marking method and system for a neuromedical image, and the method comprises the following steps: obtaining a brain nerve image, segmenting a left brain area and a right brain area, carrying out the gray correction, extracting a contour boundary, carrying out the calibration, repairing a broken contour, dividing grids, and recognizing a texture extension path. And matching the transparency level and performing visual rendering to obtain a visual marking result of the glioma region. According to the invention, gray correction is carried out through the symmetry of the brain, the accuracy and the anti-interference capability of boundary point identification are improved by combining a boundary screening mode driven by gray difference, an angle change continuity detection mechanism is adopted, the integrity and the path stability of a curvature structure are enhanced, and a rasterized texture path extraction mode is adopted. And realizing continuous expression of cross-regional texture features, and optimizing a visual hierarchy and detail comparison effect of the marked region in combination with a transparency setting scheme of gray offset matching.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the technical field of three-dimensional modeling in engineering surveying, in particular to a point cloud building component modeling method and system based on feature extraction. Three-dimensional point cloud data of building components are obtained, a multi-scale local neighborhood calculation covariance matrix is constructed with each point as the center, eigenvalues are decomposed, an edge probability graph is generated, corresponding component categories are semantically output, and corresponding key feature point sets are screened; taking the feature point cloud as a control point, constructing a three-level B-spline surface model, and adjusting the corresponding spatial distribution density; a weighted complete graph is formed through a key feature point set, a non-planar area is identified and processed through a Kurtowski theorem, a QEM algorithm is applied to carry out lightweight processing on a three-dimensional model, screening and optimization are carried out through automatically extracting key feature points of building components, and a concise and accurate lightweight model is constructed based on feature point topological optimization and a graph theory algorithm. The automation degree and efficiency of modeling are improved, and the contradiction between model lightweight and precision is effectively solved.

The invention discloses an interactive drawing generation method, system and device based on artificial intelligence, relates to the technical field of artificial intelligence, and realizes the generation of a high-quality image through multi-modal data fusion, semantic alignment, conflict detection and iterative correction. The scheme comprises the following core steps: acquiring multi-modal data such as texts, voices, sketches and the like, and processing the multi-modal data into a formatted feature data set; performing alignment and weighted fusion on the multi-modal features to generate unified semantic space features; detecting semantic inconsistency of the image and text description by using a vision-text attention mechanism, generating a conflict index matrix and triggering an alarm mark; and iteratively correcting the generated image based on the unified semantic space features, the conflict index matrix and the alarm marks. The system is composed of a data acquisition and preprocessing module, a multi-modal feature alignment and fusion module, a conflict detection and alarm module, an image generation and iterative correction module, a system control and scheduling module and the like.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The invention discloses a deconvolution-based full-focusing super-resolution imaging method, and relates to the technical field of full-focusing super-resolution imaging, and the method specifically comprises the steps: 1, carrying out the data collection based on a full-matrix collection mode, and carrying out the initial image reconstruction through a full-focusing method, and 2, carrying out the reconstruction of an initial image through the analysis of a point spread function of an imaging system, the method comprises the following steps: step 1, establishing a physical convolution model to describe a fuzzy effect in an imaging process, and step 2, by taking minimization of an imaging error as a target and introducing sparse constraint, promoting generation of non-zero reaction only at a position where a defect actually exists in a reconstructed image and inhibiting background noise and artifacts. A point spread function modeling imaging process is introduced, sparse deconvolution solution is carried out on defect distribution by utilizing a fast iteration threshold method, and a super-resolution imaging scheme which has a physical basis and is high in robustness and calculation efficiency is provided for guided wave ultrasonic imaging.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

A computer-implemented method for processing electronic images for subject-specific two-dimensional modeling of a subject's vasculature, comprising: receiving one or more subject-specific three-dimensional models of the subject's vasculature, wherein the subject-specific three-dimensional model includes a representation of the vascular tree structure; determining a two-dimensional viewing plane; determining a projection of the representation of the vascular tree structure of the one or more subject-specific three-dimensional model onto the two-dimensional viewing plane; generating one or more subject-specific three-dimensional models around the representation of the vascular tree structure; and generating a two-dimensional image depicting the one or more generated models.

Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

An apparatus to facilitate lossless and lossy automatic hardware compression in graphics-to-graphics network links is disclosed. The apparatus includes compressor / decompressor circuitry (CDC) integrated with physical layer (PHY) intellectual property (IP) hardware circuitry for a graphics processor unit (GPU)-to-GPU communication link communicably coupling a first GPU to one or more other GPUs, the CDC to: receive a data message from the first GPU, wherein the data message is in an uncompressed format; determine that a compression process is to be applied to the data message; apply the compression process to the data message to generate a compressed data message; and cause a GPU link IP hardware circuitry that comprises the PHY IP hardware circuitry to transmit the compressed data message over the GPU-to-GPU communication link.