189 results about "Geometric reconstruction" patented technology

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention provides a scene reconstruction and semantic understanding method and system based on a visual large model, and relates to the field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining multi-view image data of a target scene, inputting a pre-training visual large model, and outputting a joint feature representation and attention weight matrix; generating three-dimensional coordinate values and semantic probability distribution of spatial sampling points in a three-dimensional space according to the joint feature representation, and constructing a spatial semantic field; clustering the spatial sampling points by using the attention weight matrix, performing semantic consistency enhancement, and converting the spatial sampling points into deterministic semantic tags; constructing a geometric optimization objective function, and adjusting three-dimensional coordinate values; and extracting continuous space sampling points with the same semantic tag to form an object boundary, constructing a scene topological graph, deducing a scene functional structure, and generating a navigation path. According to the method, the unification of accurate geometric reconstruction and deep semantic understanding of the scene is realized, the three-dimensional reconstruction precision and semantic analysis accuracy are improved, and reliable support is provided for intelligent navigation.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a VR scene automatic generation method based on a parallel projection design drawing, and the method comprises the following steps: S1, importing a two-dimensional CAD drawing containing building design information, and analyzing a primitive set in the drawing; s2, performing component type identification on the primitives, and establishing a mapping relation between a primitive set and spatial semantics; s3, performing geometric reconstruction based on a mapping relation between the primitive set and spatial semantics, and generating a three-dimensional component model with a unique name identifier; s4, establishing a multi-stage mapping model, and realizing size conversion from a drawing scale to a space coordinate system of the virtual reality system; s5, completing three-dimensional coordinate positioning and automatic assembly according to the multi-stage mapping model, and generating a three-dimensional structure layout of an overall building or park level; and S6, importing the assembled three-dimensional structure layout into a virtual reality rendering engine to generate a virtual reality scene. According to the invention, an automatic, precise and integrated conversion process from a two-dimensional design drawing to a three-dimensional virtual reality scene is realized.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly provides a shielding object three-dimensional reconstruction method and system based on millimeter wave radar and vision fusion. The method comprises the following steps: acquiring radar point cloud data and visible light image data of a target object; performing space-time alignment on the radar point cloud data and the visible light image data, and establishing a space mapping relation of multi-modal data; generating a three-dimensional geometric structure of the shielded part through a geometric reconstruction model based on three-dimensional space information penetrating through the shielding object in the radar point cloud data; based on the context features of the visible light image data, generating surface textures of the occluded area through a texture completion model; and fusing the three-dimensional geometric structure and the surface texture, and outputting a three-dimensional reconstruction model containing complete information of the occlusion region. Geometric consistency and texture authenticity of virtual-real fusion are guaranteed through space-time alignment and multi-modal data mapping, and the reliability of three-dimensional modeling in a complex shielding scene is remarkably improved.

The invention relates to the technical field of visual perception, and discloses a mechanical arm visual grabbing method and system. Comprising the following steps: collecting depth images of a target scene at different visual angles in real time, and generating initial point cloud data by using all the depth images; processing the initial point cloud data by using a double-flow neural radiation field to generate a geometric field and a capture field; a preset dynamic updating mechanism is adopted, combined dynamic optimization is conducted on the geometric field and the grabbing field, and optimal grabbing parameters corresponding to all moments in the process that the mechanical arm executes the current grabbing task are obtained; and the mechanical arm is controlled to execute the grabbing action based on the optimal grabbing parameters corresponding to all the moments. According to the method, high-precision three-dimensional scene representation is constructed through depth fusion of multi-view depth images, so that the integrity and robustness of geometric reconstruction of an object in a complex environment can be remarkably improved; based on joint modeling of a double-flow nerve radiation field, collaborative optimization of a three-dimensional structure and grabbing parameters of an object is achieved, and stability and adaptability of grabbing performance in a dynamic environment can be effectively improved.

The invention relates to the field of computer graphics, reverse engineering and additive manufacturing, discloses a multi-modal fusion and physical constraint three-dimensional point cloud geometric modeling system and method, and aims at overcoming the defects that traditional three-dimensional reconstruction entity attributes are missing, key features are prone to being lost, semantic understanding does not exist, and process automation is low. The system comprises a point cloud preprocessing module, a semantic segmentation and recognition module, a mixed geometric reconstruction module, a multi-component assembly and physical constraint solution module and an entity model output module. The method comprises the following steps: denoising and sampling an original point cloud, realizing semantic and instance double segmentation through a Point Net + + network, endowing a geometry-function composite label, completing hybrid reconstruction through improved Poisson reconstruction, parameterized B-Rep fitting and standard component library matching, and outputting a multi-format engineering available model after physical constraint verification and automatic correction of a violation model. According to the method, automatic, high-fidelity and high-practicability conversion from the point cloud to the engineering-level entity model is realized, and the method is adaptive to multiple industrial application scenes.

The invention provides a rapid calibration method and system for angle measurement precision of a laser seeker. The method comprises the following steps: emitting a laser beam to a calibration rod array through a laser seeker, and collecting the displacement of a laser spot reflected by the surface of each calibration rod through a position sensitive device; inputting the laser spot displacement into a spot displacement feature extraction network, and generating an angle and displacement time sequence containing a timestamp; identifying abnormal sampling points in the angle and displacement time sequence; and performing geometric reconstruction on the abnormal sampling points by adopting a space geometric constraint optimization algorithm and combining a fixed space included angle constraint relation to obtain angle measurement error calibration parameters of the laser seeker. According to the technical scheme provided by the invention, the high-precision dynamic calibration of the angle measurement error of the laser seeker is realized through the collaborative analysis of the space coordinates of the calibration rod and the displacement of the laser spot in combination with the neural network and geometric constraint optimization.

The invention discloses a scene three-dimensional reconstruction and vector information extraction method and device based on vehicle return data, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of a time sequence image returned by a vehicle and corresponding pose data, and obtaining a semantic mask and a camera pose of each frame of image in the time sequence image; based on the semantic mask and the camera pose, performing three-dimensional geometric reconstruction on the static scene area in the time sequence image to obtain a static three-dimensional scene model; a dynamic target is separated from the time sequence image according to the semantic mask, three-dimensional motion modeling and independent three-dimensional reconstruction are carried out on the dynamic target, and a dynamic target three-dimensional model is generated; and generating a dynamic three-dimensional scene model and vector labeling information based on the static three-dimensional scene model and the dynamic target three-dimensional model. According to the method, the limitation of automatic driving shadow mode data is overcome, the three-dimensional reconstruction of the scene and the automatic generation of the true value information are realized, the data acquisition cost is further reduced, and the efficiency of automatic driving research and development is improved.

The invention discloses a contact force field-based topological optimization design method for a detachable steel structure beam-column joint reinforcing member, which comprises the following steps of: (a) establishing an integral joint finite element model, defining a frictional contact relationship and carrying out nonlinear static analysis; (b) extracting node counter-force components on all contact surfaces and taking the node counter-force components as equivalent loads acting on a reinforcing component; (c) establishing a reinforcing member independent optimization model, applying an equivalent load and defining an inertia release boundary; (d) setting a design variable, a constraint condition and a target function of topological optimization, and solving; and (e) carrying out geometric reconstruction and performance verification on an optimization result. According to the method, a complex contact nonlinear problem is converted into a determined load boundary condition, an inertia release method is utilized to deal with a constraint problem in optimization ingeniously, an optimal topology result of the detachable reinforcing component is obtained finally, and the problem of optimization result distortion caused by the uncertain boundary condition is effectively avoided.

The invention discloses a three-dimensional point cloud geometric information compression method based on implicit neural representation, and the method comprises the steps: constructing a trunk structure of an implicit neural network through a plurality of sine representation network layers which are connected in series, and introducing a variable-scale position coding mechanism on this basis, the method enables a network to obtain higher geometric reduction precision while keeping a compression ratio, and comprises the following steps: (1) inputting space coordinates of divided voxels into a position coding module with adjustable scale parameters; (2) feeding a coding result into an implicit neural network constructed by a network layer based on sine representation, and outputting the occupancy probability of the voxel through an activation function; (3) in a training stage, the model continuously optimizes parameters, so that the output probability distribution is highly consistent with a real occupied label; (4) after model training is completed, a method of combining an AdaRound second-order quantization optimization strategy and quantization perception training is introduced, network weight is finely adjusted, and quantization errors are reduced; (5) in a reasoning stage, judging whether the voxel is occupied or not according to a preset threshold value, and when the prediction probability exceeds the threshold value, regarding the voxel as occupied; and (6) all voxels judged to be occupied are aggregated, and reconstruction of the geometric structure of the point cloud is completed.

The invention discloses a three-dimensional terrain construction method based on multi-modal semantic constraints, and relates to the technical field of three-dimensional terrain modelling, and the method comprises the steps: obtaining an optical remote sensing image and a digital earth surface model, constructing unified space reference data, and carrying out the joint semantic analysis of ground features under the guidance of the multi-modal semantic constraints; generating a semantic mask set with structural consistency; further performing regional geometric reconstruction on the digital earth surface model on the basis of a mapping relation between semantic categories and earth surface physical attributes to form a three-dimensional terrain entity conforming to ground feature space expression characteristics; meanwhile, by analyzing the project cost document and extracting the project cost data with spatial directivity, under the constraint of spatial consistency and semantic consistency, the project cost information and the three-dimensional topographic entity are subjected to association mapping, and finally a three-dimensional topographic data result fusing topographic geometric information and project economic attributes is formed.

The invention discloses a particle geometric reconstruction and morphological parameter automatic evaluation method based on a three-dimensional image, and the method comprises the steps: firstly obtaining a three-dimensional gray image of a particle sample, carrying out the Gaussian noise reduction, binaryzation and watershed segmentation preprocessing, and achieving the precise segmentation of independent particles based on an established particle segmentation defect repair algorithm; and then, according to the segmented and marked three-dimensional particle image, an initial contour surface is reconstructed by using a marching cube algorithm to generate a particle surface grid, noise is suppressed by using a Taubin smooth optimization algorithm, a topological structure is automatically calibrated, and a digital model conforming to real particle surface features is constructed. And finally, importing each particle model into the created particle morphological parameter automatic evaluation software to realize quantitative analysis of particle morphological characteristics.

The invention provides a liquid cooling plate flow channel design optimization method and device based on topological optimization and a liquid cooling plate, and relates to the technical field of thermal management, and the method comprises the steps: S1, determining the geometric parameters of the liquid cooling plate according to the size of a battery module, constructing a double-layer two-dimensional design domain, and setting material, heat source and boundary conditions; s2, a topological optimization algorithm is adopted, material distribution is represented with a relative density variable, a multi-objective function giving consideration to the maximum heat exchange amount and the minimum pressure drop is constructed, a clear fluid-solid interface is obtained through density filtering and a projection function, and a target double-layer two-dimensional flow channel contour is obtained through iterative solution under volume fraction constraint; and S3, taking the target double-layer two-dimensional flow channel contour as a cross section to carry out spatial stretching bending deformation, and generating a double-layer special-shaped flow channel structure with a diffusion layer and a collection layer. According to the method, global optimization of two-dimensional topological optimization and three-dimensional geometric reconstruction are fused, the contradiction between the heat dissipation performance and the flow resistance is effectively solved, and a high-performance heat dissipation solution is provided for battery thermal management.

The invention belongs to the technical field of three-dimensional surface reconstruction, and particularly relates to a robust three-dimensional surface reconstruction method based on multi-scale fusion. Acquiring three-dimensional point cloud data, fitting local implicit curved surfaces of neighborhood points by adopting a weighted least square method, and generating a geometric reconstruction grid model; extracting geometric features in different receptive fields through multi-scale convolution, introducing channel attention, space attention and a gating mechanism to perform weighted fusion on the geometric features in different receptive fields, enhancing a global dependency relationship by adopting a feature mixing operation, and generating a deep learning grid model; performing double-branch fusion on the geometric reconstruction grid model and the deep learning grid model, highlighting a geometric sensitive area, and obtaining a fused enhanced feature; inputting the fused enhanced features into an implicit function decoder, and predicting a signed distance function value of each vertex; and based on the predicted signed distance function field, a contour surface extraction algorithm is adopted to generate a triangular mesh model, so that three-dimensional surface reconstruction is completed.

The invention discloses a concrete member reinforcing steel bar identification and dislocation connection collaborative design method and system. According to the method, point cloud data of an exposed reinforcement area are collected through high-precision three-dimensional laser scanning, and precise three-dimensional geometric parameters of existing reinforcements are obtained through registration, denoising and geometric reconstruction; on the basis, a multi-target optimization model which takes bearing capacity maximization as a core target and takes distribution uniformity and symmetry into consideration is constructed, a two-stage optimization strategy is adopted to automatically solve an optimal arrangement scheme of newly-added reinforcing steel bars, and a digital result capable of being directly constructed is output after performance checking. The system comprises a data acquisition module, a point cloud processing and reconstruction module, a collaborative optimization design module and a scheme output and check module. According to the method, full-process automation from reinforcing steel bar recognition to optimization design is achieved, and the design quality and efficiency of reinforcement engineering are remarkably improved.

The invention relates to a high-fidelity three-dimensional reconstruction method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source heterogeneous data of a power grid target region, carrying out the time-space alignment and standardization preprocessing, and generating a standardized data set; performing multi-modal feature extraction and fusion coding on the standardized data set to generate uniform multi-dimensional feature representation; performing entity matching and semantic enhancement on the multi-dimensional feature representation and a pre-constructed power grid semantic knowledge graph to generate a semantic enhanced three-dimensional scene graph; carrying out three-dimensional geometric reconstruction and dynamic detail enhancement on the three-dimensional scene graph based on semantic enhancement, and constructing a high-fidelity power grid model; performing multi-level-of-detail optimization processing and format packaging on the high-fidelity power grid model, and outputting a high-fidelity three-dimensional power grid model suitable for the digital twin platform; according to the scheme, multi-source heterogeneous data can be fully fused, a high-precision and high-fidelity three-dimensional power grid model is constructed, and the requirements of fine management and real-time monitoring of a power grid are met.

The invention discloses an aircraft surface geometric reconstruction method based on deep learning. The method comprises the following steps: acquiring a multi-view two-dimensional image of a target aircraft through a camera, extracting a target area mask, and constructing a feature corresponding relation between view angles to estimate camera parameters; constructing a multi-source loss function of fusion scene prior knowledge of the neural rendering network, and inputting camera parameters and the preprocessed two-dimensional image into the network for training until loss convergence to obtain network implicit output; point cloud reconstruction of the target aircraft is carried out, and an initial reconstruction point cloud is obtained after preprocessing; and inputting the initial reconstruction point cloud into a differentiable parametric curved surface model for optimization, outputting a three-dimensional parametric curved surface model after processing, extracting appearance parameters of the target aircraft to reconstruct the surface of the aircraft, and performing geometric reconstruction. The method effectively improves the precision and efficiency of model reconstruction, has good visualization, interaction and engineering integration capabilities, and is suitable for various application scenes such as industrial design and medical modeling.

The invention provides a traffic accident scene reconstruction method and device, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining a video frame sequence of a traffic accident scene collected by an unmanned plane, carrying out the information gain evaluation of the video frame sequence, and constructing a key frame set; according to the key frame set, macroscopic geometric reconstruction and microscopic normal recovery based on airborne light source dynamic change are carried out on the traffic accident scene, and a macroscopic depth map and a microscopic normal field are obtained; constructing a fusion optimization model with the macroscopic depth map as low-frequency constraint and the microscopic normal field as high-frequency gradient guidance, and fusing the macroscopic depth map and the microscopic normal field by using the fusion optimization model to obtain a fused depth map; and constructing an accident scene reconstruction model according to the fused depth map. By adopting the traffic accident scene reconstruction method and device, key details of the accident scene can be captured, the sensitivity to micromorphology is increased, and the accuracy of traffic accident scene reconstruction is improved.

The invention discloses a large-scale three-dimensional scene reconstruction method capable of simultaneously training scene RGB and semantics, and relates to the field of three-dimensional computer vision and graphics, in particular to a three-dimensional scene multi-modal reconstruction method based on block training and hierarchical Gaussian distribution. The method comprises the steps that an RGB image of a target scene is input, a tool is used for decomposing the scene into a three-dimensional Gaussian model containing appearance, semantics, depth and other information, rough optimization and block training are conducted on the target scene, and finally a multi-modal result containing the scene, semantics and depth information is obtained through a hierarchical merging algorithm. According to the method, multi-modal information such as geometric reconstruction and semantic segmentation can be efficiently integrated, and the real-time rendering performance is remarkably improved; the method is suitable for large-scale scene modeling, virtual reality content generation and automatic driving environment perception.

The invention discloses a load balancing Gaussian sputtering method for large-scale scene geometric reconstruction. The method comprises the following steps: step 1, acquiring a multi-view RGB image sequence of a large-scale scene, and processing the multi-view RGB image sequence by using a COLMAP tool to obtain camera parameters and an initial sparse point cloud; 2, based on a pixel-level load balancing strategy, effective Gaussian primitives are screened, spatial distribution of the effective Gaussian primitives is regulated and controlled through a loss function, and redundant primitives are reduced; 3, introducing global consistent geometric prior, fusing monocular depth and sparse point cloud depth to generate a reference depth map, and constructing depth consistency and smooth loss; 4, dividing illumination areas through a multi-area exposure compensation model, adaptively correcting brightness, and constructing image luminosity loss; and 5, performing global optimization in combination with multi-dimensional loss. And 6, rendering a depth map for the trained model, generating a TSDF field by using a TSDF fusion algorithm, and extracting a scene grid to obtain a final three-dimensional geometric model. According to the method, large-scale scene high-precision reconstruction can be realized on a single GPU, memory consumption is remarkably reduced, and geometric reconstruction quality and illumination robustness are improved.

The invention discloses an underground mine pipeline type point cloud completion method based on axial slicing, which relates to the technical field of point cloud data processing, is used for performing data completion on pipeline type point clouds, and comprises the steps of axially slicing preprocessed pipeline point cloud data, performing dimension reduction on three-dimensional point clouds to two-dimensional plane processing, and calculating related geometric parameters of pipelines. The method comprises the following steps: extracting a pipeline center line based on improved DBSCAN clustering and least square circle fitting, constructing a skeleton model under geometric topology constraints, combining local geometric features and global topology constraints, realizing geometric reconstruction of a missing region through rotating surface generation and a local interpolation algorithm, and realizing robust repair of the missing region. According to the method, a convex hull and an RANSAC method are combined to extract a pipeline center line and geometric parameters, pipeline point cloud data complementation is realized through a continuous rotation correction method, and the pipeline point cloud complementation quality is remarkably improved.

The embodiment of the invention discloses a gear pump shell topological optimization method and device based on an SIMP variable density method and a gear pump shell. The method comprises the steps that a three-dimensional model of the gear pump shell is established; performing statics analysis on the three-dimensional model to obtain stress and deformation distribution under different working conditions; taking the mass minimization of the shell as an objective function, taking stress and deformation distribution as constraint conditions, taking material distribution as a design variable, and adopting an SIMP variable density method to construct a topological optimization model; solving the topological optimization model to obtain optimized material distribution; and performing geometric reconstruction on the three-dimensional model based on the material distribution to obtain a reconstructed three-dimensional model. According to the scheme, material distribution is optimized through the SIMP variable density method, on the premise that the structural strength is guaranteed, the mass of the gear pump shell is greatly reduced, the stress distribution of the optimized shell is more uniform, and the structural stability and deformation resistance of the shell under the high-pressure working condition are remarkably improved.

The invention discloses a road intelligent diagnosis method based on an AI large model, and relates to the technical field of road engineering detection, and the method comprises the following steps: building a unified time base line, constructing a phase reference field, injecting a calibration pulse, separating dynamic shadow time sequence texture and crack initial features in a road image sequence, and obtaining a road image sequence; generating prior information of dynamic shadows and cracks; under the constraint of prior information, polarization spectrum three-dimensional coding imaging is carried out, polarization angle distribution and spectrum gradient are extracted, a feature dictionary of dynamic shadows and cracks is constructed, and phase drift in the prior information is calibrated. According to the method, a unified time base line and a phase reference field are constructed, and accurate distinguishing of dynamic shadows and cracks is realized by combining polarization spectrum imaging and anti-fact distillation; the point cloud and acceleration data are fused for geometric reconstruction, and through phase conjugate extinguishing and closed-loop feedback, the continuity and accuracy of diagnosis are significantly improved, and maintenance error decisions caused by misjudgment are avoided.

The invention relates to the technical field of building geometric reconstruction and crack detection, in particular to a three-dimensional reconstruction and damage detection method for a building in an intelligent driving environment, and the method comprises the steps: collecting a building video in a road environment during intelligent driving, and reconstructing a sparse point cloud of the building through an SfM algorithm; inputting each frame of time sequence image into a pre-trained semantic segmentation model to generate a crack highlight image; according to the sparse point cloud, initializing a corresponding parameterized 3D Gaussian ellipsoid, and optimizing parameters of the 3D Gaussian ellipsoid; mapping the 3D Gaussian ellipsoid to a two-dimensional image plane through a camera model, and generating a rendered image through a differentiable rendering algorithm; and comparing the rendered image with the crack highlight image, iteratively calculating the overall loss, and transmitting the overall loss through a back propagation gradient until a preset convergence condition is reached, thereby obtaining a final 3D model which simultaneously expresses the overall structure of the building and crack details. According to the invention, accurate environment data can be provided for the automatic driving system.

The invention relates to the technical field of power line monitoring, and discloses a tree growth prediction method, system and device based on a multi-temporal point cloud and a growth model, and a medium, and the method comprises the steps: collecting the multi-temporal data of a target region, carrying out the vegetation region recognition and single tree segmentation of a vegetation point cloud, extracting the structural features of each tree, and carrying out the prediction of the growth of each tree; generating a time sequence characteristic data set; constructing a tree growth model, taking the time sequence characteristic data set as model input, introducing a point cloud structure parameter as a dynamic constraint item, and performing parameter optimization on the model to obtain a prediction curve of a multi-dimensional growth index of the tree; and based on the prediction curve of the multi-dimensional growth indexes, performing dynamic geometric reconstruction through original point cloud data, and generating an interactive visual three-dimensional model. According to the method, on the basis of multi-source heterogeneous point cloud data, the single-tree-level growth model can be constructed, the time sequence structure change trend of the single-tree-level growth model is analyzed, and the space error and the time stability of a prediction result are quantified.

The invention relates to the field of clearance checking of a vehicle chassis part DMU (digital prototype), in particular to clearance checking between parts with large deformation. Specifically, the invention provides a dynamic gap checking method based on flexible body deformation coupling analysis, and the method comprises the steps: building a flexible body nonlinear mechanical model through finite elements, applying boundary conditions such as working conditions and loads, executing large deformation and nonlinear analysis, and obtaining deformation data of an anisotropic material under a limit load; converting the deformation data into a parameterized entity model through a geometric reconstruction algorithm, and constructing a high-precision digital prototype; multi-degree-of-freedom kinematics simulation is implemented in a DMU module of three-dimensional design software, and three-dimensional space analysis is performed on dynamic gaps between flexible bodies and between flexible and rigid bodies in combination with a contact interference analysis algorithm. The method can be used for checking the clearance between the moving parts, judging the interference of the moving parts and confirming the operation stability of the vehicle in complex and severe working conditions.

The embodiment of the invention provides a power distribution network multi-scene inspection method, system and device based on an unmanned aerial vehicle and artificial intelligence and a medium, and belongs to the field of power distribution line inspection. The method comprises the following steps: scheduling an unmanned aerial vehicle cluster, cooperating with fixed monitoring and satellite remote sensing data, and obtaining multi-mode sensing data of a power distribution network line and equipment; generating a three-dimensional live-action digital twinborn body through a multi-view geometric reconstruction and semantic segmentation method; performing space-time alignment on the sensing data, the digital twinborn body, the historical standing book and the line topology to form a unified cognitive model of the power distribution network; based on the model, identifying equipment defects and topology anomalies, and generating an unmanned aerial vehicle cluster collaborative execution strategy; and optimizing the digital twin body by using the recovery operation data. A three-dimensional live-action model is generated by applying a multi-view geometric reconstruction technology, space measurement and risk point marking are automatically completed, the problem of visual angle limitation of manual investigation is solved, the operation and maintenance cost is reduced, and quality and efficiency improvement of a distribution network is realized.

The present application belongs to the technical field of communications. Disclosed are a geometry reconstruction method and apparatus, and a device. The geometry reconstruction method comprises: a decoding end decoding a first bitstream to obtain a first flag, wherein the first flag is used for indicating distribution information of original point clouds in a geometric structure corresponding to a Trisoup node; the decoding end processing vertices in the geometric structure on the basis of the first flag; and the decoding end using the processed vertices to reconstruct a point cloud.