7886 results about "View angle" patented technology

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

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 relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The embodiment of the invention discloses an intelligent surveying and mapping method and system based on AI and BIM fusion. The method comprises the steps that an unmanned aerial vehicle platform carrying a laser radar, an RGB camera and a positioning system is used for scanning ancient building cultural relics and surroundings in a multi-angle flight mode, point cloud data, multi-view image data and position and attitude data are synchronously collected, and the three are associated through timestamps; after the point cloud data and the multi-view image data are preprocessed, cross-modal registration is completed through feature matching and pose estimation in combination with the position and pose data, and a registration data set is obtained; semantic segmentation is carried out on the point cloud data and the image data in the registration data set, and semantic segmentation results are fused based on the incidence relation; classifying and aggregating the original point cloud components according to category labels, constructing a topological relation reasoning assembly relation, calling corresponding BIM template instantiation model components based on the assembly relation, and hooking a segmentation result to generate a semantic enhanced BIM model; and integrating the BIM model and the GIS base map to form a fusion model so as to plot the historic building cultural relics.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention discloses a dynamic scene three-dimensional reconstruction method and device based on hydrogen energy unmanned aerial vehicle survey, and the method comprises the steps: obtaining dense time sequence multi-view image data of a target region through a hydrogen energy unmanned aerial vehicle platform, and carrying out the preprocessing of radiation correction and geometric correction; carrying out optical flow analysis and deformation rate clustering on the preprocessed image, identifying a pseudo-static anchor point and constructing a dynamic reference field; introducing a dynamic reference field as a soft constraint in a binding adjustment process, and optimizing a camera pose to generate a three-dimensional point cloud with consistent time and space; and finally, mapping the point cloud to a space-time voxel grid, constructing a surface evolution model by using a graph neural network or an anisotropic diffusion algorithm, and calculating a surface deformation vector to realize continuous and high-precision three-dimensional reconstruction of the disaster scene surface deformation process.

The invention relates to the field of welding, and discloses a non-standard intelligent customized welding system based on vision and surface gradient, which comprises a visual perception module used for iteratively approaching a workpiece from a preset initial height through a self-adaptive high angle shooting exploration mechanism, and combining layered grid division based on a camera view and a progressive multi-angle expansion scanning mode, collecting multi-view three-dimensional point cloud data of the workpiece; and the point cloud processing module is used for performing regional progressive registration on the multi-view three-dimensional point cloud data, implementing threshold constraint based on point cloud curvature characteristics by dynamically adjusting registration step length, and eliminating accumulative errors in combination with pose map optimization. Through self-adaptive high-angle shooting and layered grid scanning, multi-view point cloud can be automatically collected without manually presetting a workpiece model, regional registration and curvature constraint are combined, the workpiece model is constructed, a weld joint structure is automatically extracted based on surface gradient features, traditional manual labeling is replaced, a welding track is generated according to weld joint topology, and the precision is dynamically corrected.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention provides a computer visual defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the steps that according to collected multi-angle images, the overall area of an electric control board is partitioned in combination with illumination conditions and visual angle information; performing image partitioning processing to generate a plurality of image sub-regions, and extracting texture features, brightness distribution features and geometric edge features in each image sub-region; rare defect feature enhancement processing is executed, multi-scale repeated superposition is carried out on low-frequency abnormal textures, and directional extension is carried out on edge fractures; performing difference comparison with the corresponding normal area combination features, and performing normalization correction in combination with the illumination condition and the visual angle information; multi-angle reproducibility analysis is executed, when the same suspected defect is detected at different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; according to the invention, the accuracy of defect detection is improved.

The invention provides a multi-view-oriented three-dimensional scene image reconstruction registration and optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-frame three-dimensional scene image data, extracting a multi-level feature set, constructing a cross-view-angle semantic association graph, building a feature corresponding relation, and calculating a multi-view-angle spatial transformation relation parameter. Performing coordinate system alignment on the image data to generate an initial three-dimensional reconstruction result, and performing optimization in combination with a multi-target joint optimization function and a dynamic adaptive weight regulation and control mechanism. According to the method, the precision and robustness of three-dimensional scene reconstruction are improved, and the problem of registration errors caused by large view angle difference in a complex scene is solved.

The invention relates to a substation operation risk identification method based on a multi-view video and high-precision positioning. Acquiring video data of a working site through a plurality of cameras with fixed visual angles and mobile video acquisition equipment; a high-precision positioning system is used for obtaining three-dimensional space coordinates of operators and equipment in real time; establishing a three-dimensional digital twinborn model of the substation equipment, and performing dynamic scene reconstruction based on the multi-view video stream to generate a real-time three-dimensional scene of the operation site; fusing the positioning data and the three-dimensional scene by adopting a space-time fusion algorithm to generate a dynamic digital portrait of the operator; and carrying out real-time analysis on behaviors and positions of operators by using a risk assessment algorithm based on a preset risk rule, calculating to obtain a risk assessment value, and setting a feedback mechanism to continuously optimize positioning and scene reconstruction precision. According to the invention, efficient, accurate and real-time identification and early warning of the operation risk of the transformer substation are realized, and the safety management level of an operation site is effectively improved.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

The invention discloses a construction site three-dimensional scene reconstruction method based on an unmanned aerial vehicle image and a monitoring video. The method comprises the following steps: acquiring a construction site scene multi-view image; a sparse three-dimensional point cloud and a camera pose are generated through a feature matching and motion recovery structure algorithm, and dense reconstruction is carried out to obtain a global three-dimensional point cloud; aligning the global three-dimensional point cloud with a world coordinate system by using geographic position information; estimating the position of a shooting camera in the three-dimensional point cloud, sampling a candidate view angle and rendering a virtual RGB image; based on two-dimensional feature matching of the shot image and the virtual RGB image, internal parameters and external parameters of the shooting camera are iteratively solved through triangulation and a pose optimization algorithm; monocular depth estimation is carried out on the shot image, and the shot image is converted to a measurement scale through static region depth alignment; and projecting the depth of the shot image to the three-dimensional point cloud, updating the dynamic object in real time, and fusing the dynamic object into a complete three-dimensional scene model. The method has the advantage that the real-time three-dimensional reconstruction of the dynamic scene of the construction site is realized.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

The invention relates to the technical field of robot visual perception and motion control, and discloses a motion control method and device based on physical reference, equipment and a medium, and the method comprises the steps: obtaining instruction information, a multi-view image and movable assembly pose information; processing the multi-view image according to the instruction information to generate target segmentation information; generating a scale normalization point cloud and a model estimation baseline; determining a physical reference baseline and generating a scale calibration factor; converting the scale normalization point cloud into a physical space point cloud by using a scale calibration factor; extracting a three-dimensional relative position of the target object relative to the movable component in combination with the target segmentation information; an action instruction is generated based on the multi-modal input. According to the method, physical scale alignment of the point cloud is realized through physical reference baseline calibration, so that a visual reconstruction result has real space significance, an accurate action instruction is generated, and the robot space understanding and operation precision is improved.

The invention relates to the technical field of panoramic image construction, in particular to a panoramic image reconstruction method and system based on multi-angle imaging. The method comprises the following steps: collecting a multi-angle original image based on a distributed multi-camera array, carrying out adaptive filtering denoising and adaptive panoramic imaging adjustment, and constructing a multi-angle imaging geometric constraint network; performing multi-view semantic information deviation elimination based on a multi-angle imaging geometric constraint network, and performing global semantic feature fusion to obtain a unified semantic space representation framework; identifying illumination feature information of different visual angles, performing multi-angle illumination corresponding compensation on the multi-angle original image, performing image semantic distortion correction based on a unified semantic space representation framework, and constructing a multi-angle illumination compensation image; and performing multi-scale texture structure analysis on the multi-angle illumination compensation image to generate a high-fidelity texture fusion image. According to the invention, a natural and seamless panoramic image is provided, a panoramic scene is perfectly presented, and the immersive visual experience of a user is improved.

The invention relates to the technical field of constructional engineering, and discloses a concrete crack intelligent identification and analysis platform based on image and point cloud fusion, the platform operates a concrete crack intelligent identification and analysis method, and the method comprises the following steps: S1, synchronously collecting image data and point cloud data of a concrete structure in the same scene; s2, establishing a unified world coordinate system and generating a depth map corresponding to the image; s3, generating image domain crack candidates; s4, generating a depth domain crack candidate; s5, performing weighted fusion on the image domain crack candidate and the depth domain crack candidate to generate a fusion crack response; s6, extracting a crack skeleton; s7, obtaining a crack three-dimensional model; and S8, selecting an optimal view angle to trigger re-acquisition of the crack area. Through a closed-loop feedback mechanism, an optimal view angle is selected for re-acquisition by calculating a comprehensive utility value after preliminary acquisition, so that information insufficiency caused by illumination, angle or sparse data is effectively made up.

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

The invention discloses a multi-modal three-dimensional target detection method and device based on image-guided depth completion and multi-stage iteration fusion, and the method comprises the steps: firstly, predicting a dense depth map through an image-guided depth completion module by using the context information of an image, and carrying out the image-guided depth completion; the depth map is fused with a sparse depth map generated by the laser radar in a mask guiding manner, so that a high-quality complemented depth map is generated, and the accuracy of subsequent view angle conversion is improved; and then, through a multi-stage iterative fusion module, iterative fine-grained fusion is carried out on the converted image aerial view features and point cloud aerial view features, so that modal conflicts are effectively relieved, and the expression ability of fusion features is enhanced. According to the invention, through accurate depth information completion and efficient multi-modal feature fusion, the precision and robustness of three-dimensional target detection can be significantly improved, and especially the effect is more obvious when a long-distance target or a blocked target and other difficult targets are processed.

The invention discloses an occlusion state 3D human body posture estimation method based on multistage optimization. According to the method, a plurality of synchronously calibrated cameras are used for acquiring RGB images, and a plurality of data enhancement strategies including random rotation, horizontal overturning, geometric shielding, object shielding and the like are introduced, so that the robustness of a 2D joint point detection network under complex visual angle and shielding conditions is improved. And then an initial three-dimensional human body posture is preliminarily estimated by using a voxel space back projection method through the detected multi-view 2D heat map. For the occlusion problem, a visibility evaluation model fusing autologous occlusion and visual angle occlusion is constructed, and robust and stable human body three-dimensional attitude estimation can still be realized under the severe occlusion condition by introducing multiple constraints such as visual consistency, time sequence continuity, attitude priori and skeleton consistency to optimize and predict a 3D attitude in a multi-stage manner. According to the method, high-precision and shielding-robust 3D joint point detection can be realized only by inputting a multi-view-angle RGB image during operation, and the method is suitable for a real scene with a complex shielding condition.

The invention discloses a complex terrain three-dimensional modeling and earthwork volume calculation method based on a multi-source fusion point cloud, belongs to the technical field of surveying and mapping and engineering surveying, and mainly solves the problems of low terrain modeling precision and insufficient earthwork calculation efficiency in a complex scene. The method comprises the following steps: constructing a ground-air integrated multi-source sensing network to synchronously acquire laser point cloud, multi-view images and positioning data; adopting PointNet + +-based initial registration and multi-scale ICP fine registration fusion to generate a unified point cloud; combining semantic segmentation and penetration probability filtering to accurately extract a digital elevation model; constructing a similar triangular prism voxel model by using a constrained Delaunay triangulation network; and finally, the earth volume is rapidly calculated by adopting a GPU parallel voxel cutting and filling algorithm, so that high-precision terrain modeling and rapid engineering quantity calculation are realized, and the method can be efficiently and reliably applied to large-scale projects such as roads and mines.

The invention discloses an irregular particle three-dimensional shape measurement device and method based on speckle imaging, and belongs to the field of irregular particle three-dimensional shape measurement, and the irregular particle three-dimensional shape measurement device comprises an irregular particle shakeout device which is used for enabling irregular particles to fall in a sparse free falling body form, and is used for forming lamellar laser vertical to the falling direction of the irregular particles, the laser sheet generating module is used for forming complete speckle distribution on the surfaces of the irregular particles; the laser sheet generating module is used for generating a laser sheet, the multi-view image collecting module is used for collecting speckle distribution on the surfaces of irregular particles in a multi-view mode and forming speckle images, the synchronous control system is used for controlling the laser sheet generating module and the multi-view image collecting module and synchronizing the time of the laser sheet generating module and the multi-view image collecting module, and the three-dimensional reconstruction processing unit is used for obtaining three-dimensional shape data of the irregular particles according to the speckle images. According to the invention, the irregular particles can be rapidly, non-destructively and in-situ measured so as to meet the requirements of aeroengine erosion research on the precision, efficiency and integrity of irregular particle data.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention discloses an image rendering method and system fused with three-dimensional perception, and aims at efficient three-dimensional expression and micro-rendering tasks of a complex scene, and improves the modeling capability of a potential space for scene geometry and radiation characteristics by fusing a three-dimensional geometric perception mechanism and a reconstruction optimization mechanism of potential radiation field modeling. And the cross-view consistency and the structure expression precision are enhanced.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.