10239 results about "View angle" patented technology

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition are solved.

The invention discloses a mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

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.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

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.

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

The invention discloses an automobile leather defect detection method and system based on visual inspection, and relates to the technical field of industrial visual inspection, and the method comprises the steps: reconstructing the three-dimensional shape of a leather surface and generating a three-dimensional point cloud picture by analyzing the parallax relation and illumination direction reflection characteristics among multi-view automobile leather images; automobile leather surface curvature change characteristics of the three-dimensional point cloud picture are extracted, multi-scale texture analysis is carried out, and potential defect areas are identified and marked; through a three-dimensional shape measurement method, defect three-dimensional shape characteristics of the potential defect area are extracted, and defect three-dimensional geometric parameters are calculated; by analyzing defect geometrical characteristics and spatial distribution rules of the defect three-dimensional geometrical parameters and utilizing a preset grading judgment rule to divide defect grades, an automobile leather quality evaluation report containing defect three-dimensional coordinates is generated; according to the method, through combination of curvature-texture multi-scale fusion detection, the recognition capability of complex surface defects is remarkably enhanced.

The invention relates to the technical field of intelligent warehousing and logistics automation, in particular to a forklift dynamic path planning method based on deep reinforcement learning, and the method comprises the steps: deploying multi-view vision, geomagnetism and other multi-source sensors, achieving data calibration and fusion through employing an insect compound eye-imitating vision model, and constructing a high-dimensional state space vector; heuristic models such as migrant bird navigation and biological stress response are adopted, deep reinforcement learning is combined, decision instructions are generated from the three aspects of path planning, dynamic obstacle avoidance and energy efficiency management, actions are executed through a control system, and deviation is fed back; a reward function is used for evaluating the decision effect, rewards are formed by weighting path efficiency, obstacle avoidance success and energy consumption penalty, the weight can be updated in a self-adaptive mode, and therefore the deep reinforcement learning model is optimized. According to the method, the accuracy, safety and efficiency of forklift path planning are effectively improved, the method can adapt to complex dynamic environments, and the requirements of intelligent logistics and industrial automation for forklift intelligent operation are met.

The invention relates to the technical field of traditional Chinese medicinal material defect detection, in particular to a traditional Chinese medicinal material defect detection method based on vision, which comprises the following steps: regularly acquiring a time sequence image of a traditional Chinese medicinal material sample, acquiring a multi-view image, carrying out pixel alignment on the time sequence image, and carrying out structured organization on the multi-view image according to a shooting direction to associate camera parameters; and generating a registration time sequence image sequence and a multi-view image set. According to the method, the time sequence images of the traditional Chinese medicine samples are collected regularly, pixel alignment is carried out, image offset caused by environment illumination fluctuation and equipment jitter is eliminated, and time-space consistency of dynamic variable quantity calculation is ensured. Structured organization is carried out on multi-view-angle images according to shooting directions, camera parameters are associated, a geometric constraint relation between view angles is established, and the problem that three-dimensional reconstruction precision is insufficient due to view angle isolation in a traditional method is solved.

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 provides an end-to-end automatic driving control method and device based on multi-camera fusion, and multi-view target detection and tracking are realized through spatial transformation and coordinate mapping by combining front wide-angle camera information and left and right wide-angle camera information. A multi-view feature fusion network architecture is designed, the multi-view feature fusion network architecture comprises three sub-networks of feature extraction, dynamic weight distribution and feature fusion, and the fusion weight is dynamically adjusted based on image definition, detection confidence and view overlapping degree. A geometric consistency constraint between visual angles and a reconstruction loss function are introduced, a deep neural network model is constructed, abnormal conditions such as camera shielding are effectively handled, and an accurate control instruction is output. According to the method, the defects of the traditional technology in the aspects of multi-view information fusion, shielding processing and the like are overcome, and the sensing ability and the control reliability of the automatic driving system are remarkably improved.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is 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 the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention relates to the technical field of visual inspection, in particular to a multi-mode industrial product dynamic defect detection system based on edge calculation. According to the method, by introducing a multi-modal image construction and enhancement mode, three-dimensional acquisition and enhanced expression of detail information of the surface of the wind power blade are realized, and by means of inter-modal image synchronization and a space registration mechanism, the consistency extraction capability of defect information under different sensor view angles is improved; through combination of density change trend analysis and direction mutation identification means, surface micro cracks, edge damages and other structural changes can be accurately identified in continuous frames, and further through multi-source discrimination and mutual elimination comparison of abnormal indexes, environmental interference and misjudgment risks are effectively eliminated, and the accuracy of the detection result is improved. Therefore, a stable defect track area with direction consistency and distribution continuity is screened out, accurate detection and partition identification of the surface defects of the wind power blade under the dynamic condition are achieved, and the reliability and precision of defect positioning are remarkably improved.

The invention relates to the technical field of visual automatic calibration, and discloses a joint manipulator automatic calibration method and device based on a visual system. The method comprises the following steps: carrying out multi-view image acquisition on a joint manipulator of the five-axis robot to obtain original image data; performing occlusion region extraction on the original image data to obtain a joint occlusion region set; on the basis of the joint occlusion region set, disordered image splicing and mark point feature extraction are carried out on the original image data, and joint feature image data are obtained; constructing an adaptive calibration equation based on the joint feature image data, and solving a target transformation relation between a global visual coordinate system and a local visual coordinate system; multi-joint collaborative calibration and joint chain constraint optimization of the five-axis robot are executed according to the target transformation relation, and joint error correction parameters are generated, high-precision joint error correction parameter calculation is achieved, and the positioning precision and the movement precision of a joint manipulator of the five-axis robot are greatly improved.

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 3D object reconstruction method combining multi-view fusion and a neural network, and the method comprises the following steps: collecting a multi-view image of a target object, carrying out the geometric calibration and view parameter calibration, and generating a standardized image sequence; inputting the image sequence into a feature extraction and voxel fusion module to obtain a preliminary three-dimensional space representation body as a coarse reconstruction model; performing uncertainty evaluation on the coarse reconstruction model, generating a voxel-level confidence coefficient heat map, and dividing the voxel-level confidence coefficient heat map into a plurality of confidence coefficient intervals; based on the confidence interval, constructing an adaptive repair network with a multi-scale residual path, and outputting and activating different repair paths as required by using a path gating mechanism; and fusing the residual output of each repair path with the coarse reconstruction model to generate an optimized final three-dimensional reconstruction model. According to the method, the risk of excessive repair or error repair can be effectively reduced, the adaptability of the model to complex areas such as sheltered areas is enhanced, and the integrity and precision of the whole three-dimensional reconstruction model are improved.

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

The invention discloses a 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 discloses a reflecting object inverse rendering method, system and device based on two-dimensional Gaussian sputtering and multi-mode diffusion prior and a medium. The method comprises the steps that images and camera parameters of a reflecting object under multiple visual angles are acquired; calculating an observation angle according to the camera parameters; performing rasterization rendering on the images of the reflecting object under the multiple visual angles by using the trained two-dimensional Gaussian primitives under the observation visual angle to obtain a rendered image under the new visual angle; according to the method, two-dimensional Gaussian is adopted as a scene representation element, surface characteristics are better fitted, and geometric reconstruction precision can be remarkably improved; the trained two-dimensional Gaussian primitive is closer to a real three-dimensional scene, and experimental tests of multiple data sets show that the method not only can accurately reconstruct geometric and material distribution of a high-reflection area, but also can recover clear environment illumination with high-frequency details, realizes efficient and real reflection inverse rendering, and has a good application prospect. The method can be widely applied to downstream tasks such as three-dimensional reconstruction, material editing and relighting.

The invention 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 scene topology understanding method and device, a storage medium and a program product, and relates to the field of computer systems based on a specific calculation model, and the method comprises the steps: inputting a multi-view environment image set into a backbone network, and generating bird's-eye view features corresponding to the environment image set; calculating a spatial transformation matrix of the aerial view features at the current moment and the aerial view features of the previous K frames, and performing space-time alignment on the obtained K + 1 frames of aerial view features to obtain multi-frame fusion features; inputting the multi-frame fusion features into a map prior model to obtain aerial view correction features; decoding the aerial view correction features based on a topological decoder, and generating a lane topological graph; comparing the lane topological graph with the annotation data, calculating an error loss function, and optimizing network parameters based on the error loss function; and generating an optimized topological graph, and determining a scene topology result based on the optimized topological graph. By implementing the method, the environment topology understanding capability in a complex scene can be improved, and the generation precision of the lane topological graph is optimized.

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.