74 results about "Motion recovery" patented technology

The invention discloses a three-dimensional geographic environment real-time intelligent deduction method based on a multi-modal large model, and relates to the technical field of computer vision. The method is used for solving the technical problem of unified modeling and real-time deduction of a dynamic object and a static environment in a three-dimensional geographical environment. The method comprises the following steps: firstly, resolving a camera pose through a motion recovery structure algorithm to generate a sparse point cloud, and separating a dynamic foreground object in a monitoring video to extract motion features; thirdly, initializing a three-dimensional Gaussian distribution set based on the sparse point cloud, extracting semantic features through a visual encoder, and mapping the semantic features to corresponding Gaussian distribution; thirdly, a topological graph structure of Gaussian distribution is constructed, motion features are used as initial excitation, and coordinate offset and appearance variation of each distribution are iteratively updated through message passing calculation; finally, Gaussian distribution attributes are dynamically updated, a continuous deduction image sequence is synthesized through micro-rasterization rendering, and high-reality real-time simulation of dynamic evolution of the three-dimensional geographical environment is achieved.

The invention discloses an unmanned aerial vehicle bridge detection method based on machine vision. According to the method, an unmanned aerial vehicle is used as an autonomous mobile platform, and full-coverage and high-resolution image data acquisition of structural surfaces such as a bridge deck, a beam body and a bridge pier is realized through systematic task planning. The collected data is processed by a motion recovery structure and a multi-view three-dimensional algorithm to generate a high-precision three-dimensional live-action model and a digital orthophoto map. On the basis, a deep learning target detection algorithm is adopted to perform intelligent analysis on the image, apparent defects such as cracks, spalling and corrosion are automatically identified, positioned and classified, and accurate quantification of parameters such as crack width and spalling area is realized. According to the method, the problems of high risk, low efficiency, high subjectivity, existence of detection blind areas and the like in traditional manual detection are effectively solved, and key technical support is provided for digital and intelligent transformation of bridge operation and maintenance management.

The invention provides a disaster site-oriented multi-unmanned aerial vehicle rapid three-dimensional reconstruction and decision-making auxiliary method and related equipment, and belongs to the technical field of emergency surveying and mapping and three-dimensional reconstruction. The method comprises the steps that multiple unmanned aerial vehicles are used for cooperatively collecting image data of a disaster site, and uncertainty indexes used for representing the data missing degree are constructed; taking the uncertainty index as input, selecting an optimal viewpoint to generate a supplementary collection task and executing the supplementary collection task based on the contribution degree of candidate viewpoints to reduction of uncertainty and the comprehensive cost of executing the viewpoints, and obtaining an image data set with coverage integrity; screening a key frame set for three-dimensional reconstruction from the image data set; performing motion recovery structure and space partitioning based on the key frame set, and fusing depth priori and semantic information to obtain an initialized point cloud; and based on a three-dimensional Gaussian sputtering model, extracting structured elements, constructing a risk cost map, and obtaining a rescue route and a disaster situation decision report. According to the invention, emergency command and rescue actions can be supported.

The invention discloses a small sample new view synthesis method based on reinitialization three-dimensional Gaussian splashing. The method comprises the following steps: firstly, acquiring sparse three-dimensional point cloud and camera internal and external parameters from a training view through a motion recovery structure algorithm; then, sampling points are generated in the point cloud bounding box by adopting a spatial expansion hybrid sampling strategy, and a coarse-grained Gaussian set is constructed and optimized; thirdly, obtaining a rendered image through Gaussian initialization and splash rendering, calculating pixel importance through depth errors and transmissivity, generating a fine-grained Gaussian set through back projection, and optimizing the fine-grained Gaussian set; and finally, calculating a sampling probability based on the cross-view contribution degree, screening key Gaussian distribution, and optimizing to form a final Gaussian set, thereby realizing high-quality new view synthesis. According to the method, the problem of sparsity difference is solved by eliminating extended view dependence, the multi-view consistency and local geometric details of a new view scene are improved, and high-quality new view synthesis of sparse data is realized.

The invention discloses a multi-view three-dimensional reconstruction method for a slender object based on curve guidance. According to the method, geometric prior and a deep learning segmentation network are fused, two-stage foreground extraction, a curve-guided motion recovery structure (SfM) and a surface optimization module capable of differential Poisson reconstruction are provided, and a set of complete processing flow is established for the problem that a slender and weak-texture object is difficult to accurately reconstruct. The method comprises the following steps: acquiring a multi-view image through a consumer-level terminal and performing frame extraction to obtain an input data set; generating a high-quality foreground mask by utilizing depth estimation and semantic segmentation; performing curve-guided SfM initialization based on the foreground skeleton curve to realize joint estimation of the camera pose and the sparse three-dimensional curve; carrying out surface geometric optimization through a micro Poisson equation and differentiable grid rendering; and designing a multi-branch loss function fusing luminosity, mask, regularization and Gaussian rendering supervision to train the model. Finally, the method can output a three-dimensional reconstruction result of the slender structure with high geometric accuracy, strong structural integrity and smooth surface, and has good robustness and application value.

The invention relates to the technical field of three-dimensional reconstruction and rendering, and particularly discloses an underwater long-sight-distance adaptive three-dimensional reconstruction and rendering method based on a Gaussian variable-distance field, and the method comprises the steps: carrying out the initialization point cloud generation based on a motion recovery structure algorithm for RGB image data obtained by underwater image collection equipment; points on the initialized point cloud are combined with a three-dimensional Gaussian distribution model, a three-dimensional Gaussian primitive is defined, and parameter initialization about information such as a Gaussian geometric center, a three-dimensional covariance matrix, opacity and color is carried out on the three-dimensional Gaussian primitive; the method comprises the following steps: establishing a Gaussian cross-space transformation model, performing transformation from a Cartesian coordinate space to a homogeneous coordinate space on a three-dimensional Gaussian primitive, establishing a Gaussian full-parameter space mapping model, mapping color information of the three-dimensional Gaussian primitive after cross-space transformation into a spherical harmonic function, and spatial adaptive three-dimensional reconstruction and rendering in an underwater long-sight-distance environment are realized.

The invention discloses a structural semantic guided bridge point cloud Gaussian splash modeling and rendering method, and belongs to the technical field of computer vision and bridge engineering digitization. The method comprises the steps of collecting a multi-view image of a bridge, and obtaining a sparse point cloud and a camera pose through a motion recovery structure technology; dense point clouds are generated, deep learning semantic segmentation is carried out, and key components such as piers, bridge floors and cables are identified; mapping the semantic tag back to the sparse point cloud; performing differential Gaussian element initialization on the sparse point cloud based on semantic tags, and endowing different parts with initial shapes conforming to geometric characteristics of the different parts; a geometric constraint loss function is introduced in the optimization process for joint optimization; and finally, outputting a Gaussian splash model of a high-fidelity and accurate geometric structure. According to the method, the problems of model redundancy and geometric distortion when a traditional Gaussian splashing technology is used for processing a bridge scene are solved, and lightweight and high-precision reconstruction and real-time rendering of the bridge model are realized.

The invention discloses a forest three-dimensional integrated reconstruction method fusing on-forest and under-forest images, and belongs to the technical field of forestry informatization, three-dimensional reconstruction and remote sensing. According to the method, two collection tasks with complementary functions are executed by deploying the same technology; an advanced unmanned aerial vehicle-based cross surrounding route is utilized to perform canopy surveying and mapping, and a crossing unmanned aerial vehicle is innovatively adopted to perform under-forest crossing collection. And carrying out independent reconstruction and high-precision registration on the two groups of homologous image data through a motion recovery structure algorithm, and finally generating a vertical structure integrated forest three-dimensional model. The result shows that the point cloud reconstructed by the CCO route is close to the airborne laser radar in integrity, and the individual tree parameters extracted by the integrated point cloud are highly related to the foundation laser radar. Through an innovative multi-view and multi-level data acquisition and fusion strategy and a single and economic photogrammetry technology, the method has the potential of obtaining comprehensive forest three-dimensional structure information, and a new method is provided for realizing large-scale and high-precision forest dynamic monitoring.

The invention relates to the technical field of computer graphics, in particular to a virtual-real fusion method and system based on Gaussian splash live-action reconstruction, and the method comprises the following steps: S1, carrying out the motion recovery structure processing of an obtained target scene image sequence, and generating a sparse point cloud of a target scene; s2, training the sparse point cloud through a Gaussian splashing technology, generating a live-action three-dimensional Gaussian model, carrying out lightweight processing on the live-action three-dimensional Gaussian model, and outputting a lightweight live-action model; and S3, acquiring an internal parameter matrix, an external parameter matrix and a real-time video stream of the physical camera. According to the method, the spatial mapping relation between the live-action reconstruction model and the artificial fine model is established, the user interaction instruction is responded, and the explicit and implicit states of different detail level models in a unified scene are dynamically controlled, so that smooth switching without context loss from macroscopic to microscopic is realized; the problems of visual angle jump and spatial cognition interruption caused by system switching in a traditional scheme are solved.

The invention discloses a power line corridor intrusion detection method based on 3D Gaussian sputtering. The method comprises the following steps: acquiring an unmanned aerial vehicle video sequence of a power line corridor and preprocessing the unmanned aerial vehicle video sequence; based on the preprocessed data, performing three-dimensional reconstruction by using a motion recovery structure and a 3D Gaussian sputtering technology to obtain a high-precision 3D GS model of the power line corridor; based on a two-dimensional image detection and segmentation result, performing semantic annotation on a three-dimensional Gaussian ellipsoid in the 3D GS model through multi-view back projection and fusion, and identifying a target object at least including the engineering machinery and the power transmission conductor; calculating the minimum space distance between the identified engineering machinery and the transmission conductor in the three-dimensional space; and judging whether intrusion occurs or not according to whether the minimum spatial distance is smaller than a safety threshold, and outputting alarm information. According to the method, the problems of low reconstruction precision, indirect measurement, poor real-time performance and the like in the prior art are solved, and real-time three-dimensional detection and early warning of the power line corridor intrusion target are realized.

The invention relates to a three-dimensional model reconstruction method and device based on a Gaussian sputtering model. The method comprises the following steps: acquiring a scene image of a sparse view angle of a target scene, and determining initialization parameter information of a Gaussian ellipsoid according to the scene image through a motion recovery structure technology and a Gaussian sputtering model; determining a priori depth, a color loss function and a gradient smoothing loss function through a monocular depth model according to a scene image; determining a reference depth according to the scene image through a multi-view three-dimensional depth estimation algorithm; determining a depth consistency loss function according to the prior depth and the reference depth; and updating the initialization parameter information through a Gaussian sputtering model based on a depth consistency loss function, a color loss function and a gradient smooth loss function, determining updated parameter information, and generating a three-dimensional model of the target scene according to the updated parameter information. The reliability and robustness of the three-dimensional model can be improved through depth prior constraint information and a dynamic gradient control method.

The application discloses a three-dimensional geographic environment real-time intelligent deduction method based on a multi-modal large model, and relates to the technical field of computer vision. The method is used to solve the technical problem of unified modeling and real-time deduction of dynamic objects and static environment in a three-dimensional geographic environment. First, a camera pose is calculated by a motion recovery structure algorithm to generate a sparse point cloud, and a dynamic foreground object in a monitoring video is separated to extract motion features. Then, a three-dimensional Gaussian distribution set is initialized based on the sparse point cloud, semantic features are extracted by a visual encoder and are mapped to corresponding Gaussian distributions. Next, a topological graph structure of the Gaussian distributions is constructed, and the motion features are used as initial excitation to iteratively update the coordinate offset and appearance change of each distribution by message passing calculation. Finally, the Gaussian distribution attributes are dynamically updated, and a continuous deduction image sequence is rendered by differentiable rasterization to realize high-fidelity real-time simulation of the dynamic evolution of the three-dimensional geographic environment.

The invention discloses an intelligent grading method and system for tunnel face surrounding rock. According to the method, a two-dimensional image, a three-dimensional point cloud and environmental mechanics data of a tunnel face are synchronously collected through a sensor array integrated on tunneling equipment; after space-time registration fusion is carried out on the multi-source data, pixel-level identification and quantitative extraction of geological features are carried out by using a deep learning model, and three-dimensional reconstruction is carried out based on a motion recovery structure algorithm to obtain rock mass structural plane occurrence parameters; visual, geometric and environmental characteristics are fused, and accurate prediction of the surrounding rock grade is realized through an integrated learning model; and finally, dynamic excavation numerical simulation is carried out by combining the three-dimensional geologic model and a prediction result, the surrounding rock stability is analyzed, and safety early warning is generated. According to the method, automation and quantification of the whole process of surrounding rock grading are achieved, and the accuracy and timeliness of tunnel construction safety decision making are effectively improved.

This application provides a method, system, medium, and device for human-centered video scene reconstruction and separation. The method includes: for a first-person perspective video sequence, initializing a 3D Gaussian set covering the background, hands, and objects based on a priori knowledge of structure recovery from motion; assigning a learnable dynamic category probability vector to each Gaussian point in the 3D Gaussian set; constructing a dedicated deformation branch; according to the learnable dynamic category probability vector of each Gaussian point, assigning each Gaussian point to the dedicated deformation branch for processing through a preset soft-hard two-stage routing mechanism, determining the Gaussian points processed by the dedicated deformation branch; rendering the Gaussian points processed by the dedicated deformation branch to determine the 4D scene reconstruction image and the decomposed reconstruction images of the background, hands, and objects. This application achieves 4D scene reconstruction of human-centered video and explicit, fine-grained separation of the background, hands, and objects.

This application belongs to the field of computer vision technology and discloses a cross-source point cloud registration method based on adversarial neural networks. The method includes: inputting a multi-frame LiDAR point cloud sequence and a UAV video sequence; segmenting each frame of the LiDAR point cloud into ground points and non-ground points and registering them to obtain a global LiDAR point cloud; performing motion recovery structure reconstruction on the UAV video sequence to generate a dense color point cloud; inputting the global LiDAR point cloud and the SfM dense color point cloud into an MMAlignNet network to construct a shared latent feature space; solving for the rigid transformation matrix from the LiDAR point cloud to the SfM dense color point cloud; and aligning the cross-modal point clouds according to the rigid transformation matrix to obtain a multimodal 3D reconstruction result. This application improves the registration accuracy, completeness, and robustness of large-scale outdoor environment 3D reconstruction by fusing UAV imagery and mobile LiDAR data, combined with cross-modal feature alignment and geometric constraint optimization methods.

The invention discloses a three-dimensional reconstruction and deformation detection method and system for industrial parts. According to the method, a multi-view image is collected through a monocular camera, and sparse point clouds are obtained through background removal and motion recovery structure processing; a three-dimensional Gaussian sputtering method is adopted for training reconstruction, and the reconstruction quality and efficiency are improved through a progressive strategy, edge tangential densification and a composite loss function; performing point cloud registration on the reconstructed model and a standard CAD model, and sequentially performing geometric center alignment, coarse registration based on multi-neighborhood feature descriptors and fine registration of introducing a robust loss function and dynamic weight optimization to realize high-precision alignment and generate an error cloud picture; and automatically outputting a deformation qualification judgment result based on comparison between the maximum error of the error cloud picture and a preset threshold value. According to the invention, the submillimeter-level requirement of industrial detection is pursued and met in precision, and the efficiency, the cost and the automation level are remarkably improved in practicability.

The invention relates to the technical field of image data processing, particularly discloses a rock mass structure information intelligent extraction method based on deep learning, and aims to solve the problems that a traditional rock mass structure surface recognition method is low in automation degree, sensitive to point cloud density change and difficult to accurately express plane direction information. A high-quality rock image is obtained through combination of multi-view unmanned aerial vehicle near-earth photogrammetry and high-dynamic range fusion and polarization calculation; reconstructing a three-dimensional point cloud by using a motion recovery structure and a plane perception improved multi-view stereoscopic vision algorithm; constructing a neighborhood by adopting farthest point sampling and ball query, and extracting hierarchical embedding features layer by layer through a point cloud deep learning network; and a plane perception hierarchical density space clustering algorithm is provided, and structural plane clustering is carried out by fusing the embedding representation similarity and the normal vector consistency. According to the method, full-automatic, high-precision and robust recognition of the rock mass structural surface and accurate extraction of occurrence parameters can be realized, and reliable technical support is provided for slope stability analysis and geological disaster early warning.

The application discloses a sparse-view 3D Gaussian splatting reconstruction method based on compressed sensing, and relates to the technical field of computer vision; comprising: step one, running a motion recovery structure from a sparse input image and a camera pose to obtain an initial point cloud, and initializing a 3D Gaussian set based on the point cloud; step two, formulating a local iterative optimization process of the 3D Gaussian splatting into a compressed sensing linear inverse problem, and taking a Jacobian matrix as a sensing matrix. The sparse-view 3D Gaussian splatting reconstruction method based on compressed sensing, by modeling the 3D Gaussian splatting optimization into a compressed sensing linear inverse problem, generates new Gaussian primitives through uniform random sampling, significantly reduces the expected cross-correlation between a sensing matrix and a vector, makes the matrix satisfy a restricted isometry property, fundamentally solves the problems of geometric ambiguity, overfitting and floating artifacts caused by deterministic midpoint splitting, and greatly improves the stability of sparse-view reconstruction.

This invention belongs to the field of visual 3D reconstruction technology and discloses a method, system, terminal, and storage medium for 3D Gaussian reconstruction of fisheye images. The method includes: acquiring a fisheye image; generating a sparse 3D point cloud corresponding to the fisheye image based on a motion recovery structure algorithm; optimizing the 3D Gaussian reconstruction algorithm based on a reversible residual network and an octahedral projection model; and reconstructing a 3D model of the fisheye image based on the optimized 3D Gaussian reconstruction algorithm and the sparse 3D point cloud. This invention reduces the 3D reconstruction error of fisheye images by simulating camera distortion parameters of a lens distortion model using a reversible residual network and incorporating these parameters into the 3D Gaussian reconstruction algorithm. Replacing the planar perspective model of the 3D Gaussian reconstruction algorithm with an octahedral projection model reduces stretching and image boundary distortion. The optimized 3D Gaussian reconstruction algorithm is used to generate the 3D model of the fisheye image, improving the realism of the 3D model reconstructed from the fisheye image.

The present application relates to the technical field of three-dimensional geological modeling and geological mapping data processing, and particularly relates to a tilt photography three-dimensional surface model geological modeling system and method, comprising data acquisition, scene construction, boundary processing, geological body modeling, profile linkage and result output modules. The data acquisition module fuses tilt photography images and terrain data to generate a three-dimensional surface model through a motion recovery structure algorithm; the geological body modeling module constructs a closed geological body through implicit surface reconstruction and constrained triangulation; the profile linkage module calls a graph neural network to predict stratigraphic intersection trends, and realizes two-dimensional and three-dimensional real-time synchronization based on event driving and sparse voxel hash mapping; and the result output module performs data verification and generates a multi-dimensional geological report. The scheme eliminates the dependence on third-party tools, improves the efficiency of geological mapping, and solves the problem of profile linkage delay.

The invention provides an automatic identification method and system for an unstable slope block under a blasting load, and relates to the technical field of stability identification, and the method comprises the steps: obtaining the physical and mechanical parameters of a target slope and a multi-angle image of the target slope; processing the multi-angle image of the target slope based on a motion recovery algorithm, and performing structural plane identification on a slope three-dimensional point cloud model obtained through processing by using a clustering algorithm to obtain all single structural planes and geometric parameters thereof; carrying out slope block identification and polyhedron modeling based on all the single structural surfaces and the geometric parameters thereof to obtain a slope polyhedron model; based on the side slope polyhedral model, stability analysis under the blasting load is carried out, and an unstable block of the target side slope is obtained through analysis through safety coefficient calculation under the blasting load. The method has high operation speed, time cost can be reduced, and performance loss can be reduced at the same time.

The method for measuring the underwater three-dimensional terrain of the sandy bed surface provided by the present disclosure comprises the following steps: building an image acquisition platform for obtaining an underwater image sequence of the sandy bed surface; arranging water and underwater control points and measuring their coordinates; determining the height, arrangement form and moving space step of the camera array; making all the cameras take pictures of the target water surface area at the same time based on the parameters determined in the previous step, and obtaining the underwater image sequence of the sandy bed surface; performing spatial calculation on the image sequence based on the motion recovery structure method, performing spatial registration only using the water control points, and then performing refraction correction, and then performing spatial registration only using the underwater control points without performing refraction correction, and then performing secondary correction on the point cloud obtained by using only the water control points and performing refraction correction through the average value of the difference between the two, and obtaining the final underwater three-dimensional terrain of the sandy bed surface. The time and space resolution and accuracy of the present disclosure are relatively high, and the observation object is not disturbed.

The application relates to a three-dimensional model reconstruction method and device based on a Gaussian sputtering model. The method comprises the following steps: acquiring sparse-view scene images of a target scene, and determining initialization parameter information of a Gaussian ellipsoid according to the scene images by using a motion recovery structure technology and a Gaussian sputtering model; determining prior depth, a color loss function and a gradient smoothing loss function according to the scene images by using a monocular depth model; determining reference depth according to the scene images by using a multi-view stereo depth estimation algorithm; determining a depth consistency loss function according to the prior depth and the reference depth; updating the initialization parameter information based on the depth consistency loss function, the color loss function and the gradient smoothing loss function by using the Gaussian sputtering model, determining updated parameter information, and generating a three-dimensional model of the target scene according to the updated parameter information. The reliability and robustness of the three-dimensional model can be improved by using a depth prior constraint information and a dynamic gradient control method.

The application discloses a Gaussian splash monocular reconstruction method based on a neural network, which comprises the following steps: firstly, constructing a Gaussian splash monocular reconstruction model comprising an image depth recovery model and an SfM motion recovery model, training the image depth recovery model, then shooting a panoramic picture, training the panoramic picture by using a spherical convolution, recovering a distorted part, then performing sparse point cloud reconstruction by using the SfM motion recovery model, reconstructing an image surface by using a Gaussian reconstruction kernel, forming a basic geometric configuration of the image, finally performing Gaussian low-pass filtering and noise reduction processing on the picture by using an image processing technology, forming a complete surface structure of an object, and completing three-dimensional reconstruction of an image scene. The method is combined with a spherical convolution kernel by using three-dimensional Gaussian splash technology to construct an object geometric model, operability is obviously improved, the calculation speed is greatly improved, and the method has important application value in the fields of intelligent medical treatment and engineering flaw detection.

The application discloses a VR-based regional landscape immersive evaluation prediction method and device, which comprises the following steps: acquiring high-resolution multi-view image data of a target region, combining a motion recovery structure with a multi-view stereo vision algorithm to construct an original three-dimensional real scene model; then, through grid simplification and space index processing, multi-detail level streaming three-dimensional data is generated, relying on a virtual reality platform to fuse terrain data, configure rendering parameters, build an interactive virtual regional environment and adapt to VR equipment; semantic score data and auxiliary evaluation data are acquired; the semantic score data and the auxiliary evaluation data are input into a pre-constructed overall landscape satisfaction prediction model to obtain a satisfaction prediction score; and the application relies on three-dimensional real scenes and VR interaction to truly restore regional landscape features; and by combining auxiliary evaluation and quantitative analysis, qualitative landscape evaluation is converted into quantitative landscape evaluation.

This invention discloses a calibration-free panoramic camera and laser point cloud registration method and system. The method includes: acquiring laser point clouds and a panoramic image; splitting the panoramic image into multiple pinhole images and performing feature extraction and matching, then recalculating the matching results back into the panoramic image; reconstructing the sparse point cloud using a motion recovery structure and obtaining the initial pose of the panoramic image; registering the sparse point cloud and the laser point cloud using an iterative nearest-point algorithm, calculating seven parameters to achieve preliminary alignment; and improving the bundle adjustment method by combining the plane normal information corresponding to the sparse point cloud with the geometric constraints of the laser point cloud to achieve precise alignment between the panoramic image and the laser point cloud. This invention does not rely on any external calibration parameters, IMU information, or prior scene information. Through image self-reconstruction and laser constraint fusion, it significantly improves the registration accuracy and geometric consistency of 3D reconstruction, and is suitable for multi-source point cloud fusion reconstruction in scenarios without auxiliary information.

The application discloses a kind of based on Gaussian splashing air-ground scene three-dimensional reconstruction method, comprising: S1, obtains the multi-source air-ground scene image containing large pitch angle aerial photograph and ground view and camera pose;S2, based on the scene sparse point cloud generated by motion recovery structure, instantiation generates initial three-dimensional space element set;S3, introduce multi-measure adaptive density control mechanism to realize the adaptive matching of Gaussian density in different scale space;S4, by camera Jacobian matrix, three-dimensional space element is projected to two-dimensional view plane, after depth ordering, along the direction of ray extraction color and opacity, by Alpha mixing formula integration output predicted two-dimensional rendering image;S5, introduce multi-dimensional geometric constraint to guarantee the consistency of physical surface;S6, after S2-S5 closed loop circulation to convergence, extract and output high-precision three-dimensional reconstruction point cloud and new view two-dimensional rendering image.

The invention discloses a snow-melting rainfall composite erosion gully remote sensing image extraction method and system, and belongs to the technical field of remote sensing image processing and geological disaster monitoring. The method comprises the following steps: acquiring a high-resolution optical satellite image and an unmanned aerial vehicle aerial survey image, and generating a high-precision digital earth surface model by adopting a motion recovery structure algorithm; constructing a spectrum-texture-terrain three-branch attention fusion network to realize erosion gully pixel-level segmentation; morphological parameters are automatically measured, and the erosion development stage is judged according to the channel head activeness and the channel connectivity; and analyzing seasonal morphological change characteristics to distinguish snow melting dominant type, rainfall dominant type and composite action type erosion gullies, and establishing an evolution prediction model.

The present application relates to rigid body motion constraint visual station vibration correction and displacement measurement method and device. The device comprises: a visual station platform; at least one measurement camera, fixed on the visual station platform, for observing a plurality of measured points arranged on the structure to be measured; at least one calibration camera or shared with the measurement camera, fixed on the visual station platform, for observing at least one control point, or no control point; the station attitude change is restored through the overall motion of the measurement point, the station position change is restored through one control point, in the case that the station does not occur position change, only through the measurement camera can restore the station attitude change, so as to realize the station motion compensation and structure real displacement measurement; a processing unit is in communication connection with the measurement camera and the calibration camera, and the processing unit is configured to perform the following operations: a) receiving and processing the image sequence collected by each camera; b) performing the method steps to estimate the motion parameters of the station and calculate the real displacement of the measured point.

The application discloses a kind of mine subsidence monitoring methods based on unmanned aerial vehicle photogrammetry and LiDAR technology, utilize unmanned aerial vehicle to carry out flight shooting to mining area, obtain mining area surface image data;Image data obtained is handled by motion recovery structure algorithm SfM, generates digital surface model DSM, orthophoto map DOM and digital elevation model DEM;Utilize airborne LiDAR system to scan mining area, obtain the three-dimensional point cloud data of mining area surface;Differential GNSS technology is used to correct and precision improvement to LiDAR point cloud data;The digital elevation model DEM generated by unmanned aerial vehicle photogrammetry is fused with the registration of corrected LiDAR point cloud data in unified coordinate system;Fused data is handled and analyzed, constructs mining area subsidence model, realizes the dynamic monitoring of mining area surface subsidence condition.The application effectively solves the problems existing in the traditional mining area subsidence monitoring method, provides strong technical support for mine geological disaster prevention, ecological environment protection and mining area sustainable development.