1064 results about "3D reconstruction" patented technology

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention, which relates to the technical field of image processing, discloses a city component automatic three-dimensional reconstruction method based on a sub-meter high-resolution remote sensing image, comprising the following steps: acquiring a sub-meter high-resolution remote sensing image of a target area, and establishing an initial ground-image projection mapping relation based on a rational polynomial coefficient model; acquiring a ground control point, correcting the mapping relation, and outputting a registration image; performing multi-view geometric matching on the registered image to generate dense earth surface point cloud, and inputting the dense earth surface point cloud into a semantic neural radiation field network to generate dense point cloud; performing cross-modal feature alignment and fusion on the dense point cloud and the vector data, and outputting a fused multi-source dense point cloud; according to the method, through fine correction of the multi-source remote sensing image, the problems of large registration error, more point cloud sparse noise and semantic deficiency are solved, and high-precision and automatic three-dimensional reconstruction of urban components is realized.

The invention discloses a sparse view three-dimensional reconstruction method and system based on dynamic occlusion perception and multi-prior fusion. The sparse view three-dimensional reconstruction method comprises the steps of obtaining a sparse view; extracting multi-scale geometric features from the sparse view through a feature pyramid network, constructing a probabilistic depth estimation module, and generating an initial sparse point cloud based on the probabilistic depth estimation module; based on the initial sparse point cloud, a dynamic occlusion perception attention mask is introduced, and occlusion area mismatching propagation is inhibited through cross-view visibility analysis and a multi-stage confidence aggregation strategy; and fusing geometric priori, semantic priori and implicit priori, carrying out multi-source priori collaborative constraint through an adaptive weight distribution strategy, enhancing the geometric accuracy of the weak texture region, repairing the occlusion region, and outputting a three-dimensional reconstruction image of the sparse view. According to the invention, the integrity and precision of sparse view reconstruction are improved.

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

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention provides a polarization three-dimensional reconstruction method and a polarization three-dimensional reconstruction system based on a prior guide diffusion model, which apply a diffusion model in the field of polarization three-dimensional reconstruction and improve the recovery capability of complex details and the robustness of noise interference resistance. According to the method, a two-stage training mode is adopted, and the generation quality and the calculation efficiency are balanced through step-by-step optimization. In the first stage, a VQGAN codec is independently trained to learn high-efficiency low-dimensional potential representation of an image, and direct high-cost calculation in a pixel space is avoided; in the second stage, learnable parameters of the VQGAN are frozen, a diffusion model is trained on a trained potential space, gradual denoising is guided through a priori condition, and potential features are generated and mapped back to an image space. The diffusion model effectively fuses the physical constraint of the polarization clue and the data prior in the gradual denoising process, and the surface normal with rich details can still be stably generated in the case of noise interference or information loss. Experimental results show that the method provided by the invention is excellent in surface normal reconstruction in a plurality of complex scenes.

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 virtual reality scene three-dimensional reconstruction method and system based on multi-source data fusion, and relates to the technical field of underground commercial and traffic integrated space scene three-dimensional reconstruction. The system constructs a multi-dimensional data set through synchronous acquisition of point cloud, images, poses, positioning and semantic text information; a three-dimensional convolutional neural network is adopted to establish an AI reconstruction fusion model, and a three-dimensional model is generated; the system calculates a structural integrity evaluation coefficient JGPG, a fusion consistency evaluation coefficient RHPG and a VR interaction adaptability evaluation coefficient VRJH, compares the coefficients with corresponding thresholds, triggers an optimization strategy, and finally completes model adaptability optimization and packaging output, thereby improving the reduction degree and interaction performance of a virtual reality scene.

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

The invention discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing. According to the method, deep learning pose estimation is combined with an underwater imaging model; firstly, a multi-frame underwater image sequence is collected as input, a deep neural network is constructed, and the network mainly comprises two core sub-modules: a pose estimation network; secondly, a three-dimensional reconstruction network is adopted, dense point cloud or voxel reconstruction is completed according to the predicted pose and image content, pose estimation and the three-dimensional reconstruction process are integrated in the same system, and overall joint optimization is achieved; and in combination with a self-adaptive underwater imaging model, modeling is performed on physical processes such as underwater illumination attenuation and scattering, so that the reality sense and the accuracy of a reconstruction result are improved. According to the method, high-quality three-dimensional reconstruction of images in a complex underwater environment is realized, and the method can be widely applied to ocean engineering, underwater robots, submarine topography surveying and mapping and underwater cultural relic protection.

The invention provides an unmanned aerial vehicle oblique photogrammetry data intelligent processing method and system, and relates to the technical field of unmanned aerial vehicle photogrammetry, and the method comprises the steps: carrying out the preprocessing of an oblique photogrammetry original image, obtaining the exterior orientation elements of a camera, carrying out the scene semantic segmentation through a deep convolutional neural network, adaptively determining the optimal three-dimensional reconstruction parameters of each region, and obtaining the optimal three-dimensional reconstruction parameters of each region. On the basis of multi-view image matching, feature point parallax information is calculated to generate dense point clouds, different areas are processed by adopting an adaptive grid division strategy and a corresponding noise reduction algorithm, and finally, a three-dimensional scene model with real textures is constructed, so that high-precision three-dimensional reconstruction of different scene areas is realized.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention relates to the field of cardiology department operations through vascular intervention, and discloses an interventional operation navigation system based on multi-modal image fusion, which comprises a preoperative three-dimensional reconstruction and planning module, which is mainly used for generating a three-dimensional model of a heart through preoperative image data, and planning the position and section of an intraoperative ICE ultrasonic probe based on the model, the intraoperative multi-modal fusion navigation module is mainly used for fusing a three-dimensional model generated before an operation with an intraoperative real-time image and providing accurate navigation support for a doctor; and the dynamic feedback optimization module is mainly used for updating a dynamic track in real time and performing safety protection. According to the invention, through the preoperative three-dimensional reconstruction and virtual path planning module, in combination with the improved 3D U-Net network and the high-precision segmentation algorithm, a three-dimensional model containing key anatomical markers is generated, 20 groups of ICE probe alternative paths are planned, seamless connection of preoperative three-dimensional anatomical information and intraoperative real-time images is realized, doctors can quickly call pre-planned paths, and the accuracy of the ICE probe anatomy is improved. Adjusting time during operation is shortened, and operation efficiency is improved.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention relates to a building refined three-dimensional reconstruction method based on airborne LiDAR point cloud, and belongs to the technical field of three-dimensional modeling. According to the method, high-quality building point cloud data is separated from original airborne LiDAR point cloud data, features and bottom contour lines related to a building are extracted, a roof surface is fitted, and the features and the bottom contour lines are projected to the same horizontal reference surface, so that correlation correction of the features and the bottom contour lines is realized; and completing preliminary reconstruction of a building model by using the corrected building bottom contour line and roof surface, constructing constraint conditions by using priori knowledge, and completing expression of detail components on the model and correction of the reconstructed building roof surface. According to the method, the problems of irregularity, distortion, cavities, difficult facade detail structure expression and the like of the reconstruction model caused by the problems of sparse, missing, cavities and the like of the building facade point cloud data in the prior art are effectively solved, and the problems of irregular edges, distortion, cavities and the like of the constructed building model are solved while efficient, automatic and refined modeling is realized.

The invention provides a depth map generation method and device based on a large model, a three-dimensional reconstruction method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, can be applied to real-time road scene depth perception, environment three-dimensional reconstruction and obstacle avoidance, and can be applied to real-time road scene depth perception. And virtual and real scene fusion and other scenes can be realized. The specific implementation scheme is as follows: performing visual coding on a monocular image to obtain a coded image; inputting the coded image and the target text into a pre-trained large language model for fusion to obtain fusion features; generating global guide features based on the fusion features, wherein the global guide features comprise joint semantic information of visual features and text features; adding noise to the color image of the monocular image to obtain a noise feature sequence; de-noising the noise feature sequence under the condition of the global guide feature, and generating an implicit feature matched with the joint semantic information; a depth map is generated based on the implicit features.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention discloses an immovable cultural relic three-dimensional reconstruction system which comprises a data acquisition module, a feature extraction module, a restoration simulation module and an optimization and decision module. The data acquisition module is used for acquiring three-dimensional data of an antique; the feature extraction module is used for extracting key features of antiques by using a deep learning algorithm; the restoration simulation module is used for constructing a virtual restoration model based on the extracted features, simulating an antique restoration process and evaluating a restoration scheme; and the optimization and decision module is used for realizing precise positioning and navigation of a repair site in combination with the SLAM technology, optimizing a repair path and assisting a repairer in making a scientific decision. According to the method, the repairing efficiency and precision are remarkably improved, automation and intelligentization of the ancient object repairing process are achieved by introducing advanced technologies such as deep learning and NeRF, the repairing efficiency and precision are greatly improved, and the repairing period is shortened.

Methods, devices, and processor-readable media for method for performing a 3D reconstruction from a single view image, comprising progressively denoising a randomly initialized set of 3D-gaussian representations with continuous guidance from an input image.

The invention discloses a three-dimensional reconstruction method based on a 3DGS self-adaptive dynamic optimization strategy and gradient perception enhancement. The compression performance and the rendering quality of a Reduce 3DGS are further optimized through multi-stage hybrid optimization; firstly, a self-adaptive loss strategy is adopted, the weight between SSIM and L1 loss is dynamically adjusted through a Sigmoid function, structure alignment is emphasized in the early stage, and enhancement detail optimization is focused in the later stage; secondly, sparsity regularization is introduced in the optimization stage, transparency entropy maximization constraint is introduced to be combined with attenuation weight, and generation of redundant primitives is actively inhibited while the redundant primitives are trimmed; finally, gradient sensing detail enhancement is carried out, gradient matching loss is increased, high-frequency texture and edge information are reserved, and detail loss caused by trimming and quantization is made up; experiments prove that the method disclosed by the invention achieves better balance in the aspects of compression ratio and visual quality.

The invention belongs to the technical field of simulation modeling, and provides a high-fidelity three-dimensional reconstruction method fusing attitude prior and geometric constraint, which comprises the following steps: acquiring an original image, and determining an initial camera attitude based on a laser radar inertial odometer; determining error constraint information and relative attitude constraint information, and optimizing the initial camera attitude according to the error constraint information and the relative attitude constraint information to obtain a fine camera attitude; inputting the original image into an image reasoning model obtained by pre-training to obtain a surface normal map output by the image reasoning model; and constructing a three-dimensional Gaussian distribution model, determining geometric constraint information, and performing iterative optimization on the three-dimensional Gaussian distribution model by using the geometric constraint information according to the surface normal map and the fine camera attitude to obtain a three-dimensional scene model. According to the method, the overall reconstruction efficiency is improved through attitude prior and geometric constraints, and the authenticity, accuracy and efficiency of the scene reconstruction process are improved.

The invention discloses a dietary pattern evaluation method and system based on digital intelligent analysis, and relates to the technical field of dietary pattern evaluation.The method comprises the steps that a mobile terminal is used for synchronously collecting multi-modal data, and the multi-modal data are preprocessed; food pictures are retrieved based on keywords to form a data set, an image recognition model is constructed, the volume and area of food materials are calculated in combination with a 3D reconstruction technology, a food material density database is matched, and automatic estimation of the quality of the food materials is achieved; building a voice interaction model fusing characters and regional voice features, and recognizing food material types and food material quality by using voice data; a dynamic allocation weight is calculated through multi-modal confidence, a graph attention mechanism is utilized to detect and correct data conflicts, and a space-time constraint rule base is combined to align dispersed intake records; and constructing a data visualization chart library. According to the method, the rise of dietary structure analysis from extensive statistics to refined intelligent evaluation is realized, and the problem of error accumulation caused by dependence on a single data source in a traditional method is effectively solved.

The invention relates to a bridge scouring pit three-dimensional reconstruction method based on a physical constraint generative adversarial network, and the method comprises the steps: collecting optical data and multi-beam sonar data, generating a joint calibration file, and carrying out the preprocessing; performing multi-source data fusion processing to obtain a training set and a test set; constructing a physical constraint generative adversarial network; and inputting the test set into the physical constraint generative adversarial network, obtaining point cloud data, and performing engineering verification. The method has the beneficial effects that through collaborative innovation of multi-modal sensing fusion and physical constraint deep learning, the problem of high-precision reconstruction of the scouring pit terrain in the complex underwater environment is effectively solved. Compared with a traditional single-mode detection or pure data driven reconstruction method, the method creatively introduces a Navier-Stokes equation as a physical constraint term of the generative adversarial network, and embeds a fluid mechanics mechanism into a deep learning framework, thereby remarkably improving the physical rationality of a reconstruction result.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a near-infrared assisted low-light scene three-dimensional reconstruction method based on 3D Gaussian splashing, which is characterized in that generation of a Gaussian ellipsoid is dominated by a near-infrared image with a high signal-to-noise ratio, and a stable geometric basis is provided for a visible light information missing area; in the rendering stage, a near-infrared rendering image and a normal visible light rendering image are respectively generated through Gaussian ellipsoid shared geometric parameters and respective opacity and color attributes of two modes; through cross-modal structure similarity loss, a normal visible light image which is forcibly rendered is aligned with a near-infrared light image in structure, and clear near-infrared structure information is used to strictly constrain the recovery process of the color of the visible light image, so that the accuracy and authenticity of a recovery result are ensured.

The invention discloses a city-level live-action three-dimensional modeling method based on air-ground multi-source data, and the method comprises the following steps: 1) respectively constructing a ground point cloud and an air point cloud, respectively extracting semantic tags, local geometric features and color features of a ground image and an air image, and carrying out the back projection of the semantic tags, local geometric features and color features to the corresponding ground point cloud and air point cloud; the method comprises the steps of (1) obtaining a ground point cloud and an aerial point cloud, (2) respectively calculating fusion multi-modal features of the ground point cloud and the aerial point cloud, (3) realizing cross-point-cloud feature interaction through a Transform point cloud registration model, and (4) selecting a three-dimensional reconstruction area. According to the invention, accurate registration of different-source point clouds with large air-ground view angle difference and low overlapping degree can be realized; the problems that an existing oblique photography urban three-dimensional model is lack of details and insufficient in precision in ground and building facade areas, and a live-action three-dimensional model is complex in updating process, long in period and low in automation degree are solved.

The invention relates to the field of computer vision and three-dimensional reconstruction, and particularly discloses a multi-view three-dimensional reconstruction method based on multi-scale feature fusion, which adopts a multi-scale feature fusion network architecture and comprises a feature extraction module, a cost body construction module, a cost body regularization network and a deep regression network. The method specifically comprises the following steps: constructing a multi-view three-dimensional network architecture based on multi-scale feature fusion; the method comprises the following steps: setting a feature extraction module, combining an FPN structure with a bidirectional feature fusion strategy, establishing a homography transformation and cost body construction module, generating a cost body, deploying a cost body regularization processing unit, carrying out global-local feature fusion by adopting a 3D UNet architecture, and executing a depth inference and refinement process. And optimizing a depth regression result through multi-stage depth hypothesis and a confidence weighting mechanism. And performing a three-dimensional reconstruction experiment and result analysis, implementing depth map fusion and three-dimensional reconstruction, and generating a dense three-dimensional point cloud model. According to the invention, the precision and integrity of three-dimensional reconstruction are effectively improved.

The invention discloses a monocular video scene dynamic three-dimensional reconstruction method based on optical flow, and relates to the technical field of scene reconstruction. Calculating an optical flow of each pixel in each frame of image in the monocular video, and determining a dynamic region mask of the image; according to the dynamic region mask of each frame of image, determining a plurality of dynamic object instances with consistent time and space; performing four-dimensional Gaussian sputtering conversion on each frame of image to obtain four-dimensional Gaussian distribution representation; for any one dynamic object instance in any one frame of image, acquiring other images containing the dynamic object instance in different image frames, and according to the coordinates of the Gaussian point clouds of the image and other images, generating a visual angle point cloud, which is not observed in the image, of the object instance; and according to color information of each frame of Gaussian point cloud in the four-dimensional Gaussian distribution representation, performing color rendering on each frame of Gaussian point cloud generating the visual angle point cloud to obtain a reconstructed scene. The method can accurately realize scene reconstruction.

The invention relates to a submillimeter-level spatial measurement system and method for a three-dimensional panoramic image of a subway tunnel, and proposes a new design of combining three-dimensional holographic modeling and traditional point cloud collection through joint adjustment of point cloud data collection and image information collection, thereby achieving high-resolution, large-view-field and global high-precision three-dimensional holographic modeling of the tunnel. The submillimeter precision can be ensured in three-dimensional reconstruction and image coordinate assignment of the high-definition image of the subway tunnel, and the problems of non-uniform precision and poor robustness during point cloud splicing caused by few point cloud features in the subway tunnel in traditional laser scanning are solved. Meanwhile, mileage and attitude information is further optimized in combination with subway tunnel high-definition images and point cloud data features, non-tunnel fixed objects existing in a tunnel three-dimensional reconstruction model are removed with the help of a machine learning algorithm, and submillimeter reconstruction and assignment of subway tunnel three-dimensional panoramic images are achieved.

The invention relates to a crowd-sourced geographic video data quality evaluation and three-dimensional reconstruction method, device and equipment. The method comprises the following steps: acquiring a to-be-processed video frame sequence; the to-be-processed video frame sequence is obtained by preprocessing public source geographic video data; constructing a video quality evaluation network; the video quality evaluation network comprises a pre-trained dynamic object detection module, a lens conversion detection module and a scene detection module; and processing the to-be-processed video frame sequence by using the video quality evaluation network, splicing the continuous scene frames output by the shot conversion detection module and the shot transition frames marked by the scene detection module according to timestamps to obtain a qualified video frame sequence, and performing three-dimensional reconstruction according to the qualified video frame sequence. By adopting the method, the quality and availability of the public-source geographic video data can be improved, and the precision and efficiency of three-dimensional reconstruction are improved.