Live-action three-dimensional model construction method and system, storage medium and equipment

Abstract

The invention discloses a live-action three-dimensional model construction method and system, a storage medium and equipment, and belongs to the technical field of three-dimensional modeling, and the method comprises the steps: laying control points in a region needing collection, and collecting ground control point data; acquiring inclined image data by using the unmanned aerial vehicle, and performing aerial triangulation calculation on the inclined image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; acquiring point cloud data by using a laser scanner, registering the point cloud data in combination with ground control point data, and then preprocessing the point cloud data to obtain laser point cloud data; carrying out fusion reconstruction on the aerial triangulation encryption point cloud data and the laser point cloud data to generate a three-dimensional model; and performing fine processing on the three-dimensional model to generate a high-precision model. According to the method, the three-dimensional laser scanning technology is used for assisting oblique photogrammetry, ground control points are used for unifying coordinates and repairing missing point clouds of a blind area, the integrity of the point clouds of an oblique photogrammetry shielded area can be guaranteed, and the fineness of the model is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a method, system, storage medium and device for constructing a real-scene three-dimensional model. Background Art

[0002] With the rapid development of geographic information technology and the continuous advancement of urban informatization, the demand for the acquisition, updating and application of geographic spatial information data is increasing day by day. Traditional means of acquiring geographic information data can no longer meet the needs of the current environment. Therefore, how to establish a three-dimensional real-scene model that can reflect actual ground elements has become a hot topic and trend in current research.

[0003] Existing 3D modeling technology mainly relies on modeling software such as Sketch Up, which is labor-intensive and time-consuming, and the modeling accuracy and authenticity need to be improved. Oblique photography technology can efficiently achieve 3D reconstruction, but due to limited perception, it is impossible to obtain detailed information such as the bottom of the building and the bridge hole, resulting in problems such as decals and missing information on the model.

[0004] Although 3D laser scanning technology has high accuracy, it is not suitable for large-scale 3D modeling due to its large amount of data, complex processing, and high cost. Therefore, there is an urgent need for an efficient, economical, high-precision, and complete 3D modeling technology solution to meet the needs of urban 3D information. Summary of the invention

[0005] To solve the above problems, the present invention provides a method, system, storage medium and device for building a real-life three-dimensional model. By using three-dimensional laser scanning technology to assist in oblique photogrammetry, the ground control points are used to unify the coordinates and repair the point clouds missing in the blind areas. This can ensure the integrity of the point clouds in the obstructed areas of oblique photography and improve the precision of the model.

[0006] The above objectives can be achieved through the following solutions:

[0007] A method for constructing a real-scene three-dimensional model comprises: arranging control points in an area to be collected and collecting the coordinates of the control points to obtain ground control point data; using a drone to obtain oblique image data, and performing aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; using a laser scanner to obtain point cloud data, and performing preprocessing after registering the point cloud data in combination with the ground control point data to obtain laser point cloud data; fusing and reconstructing the aerial triangulation encrypted point cloud data and the laser point cloud data to generate a three-dimensional model; and performing fine processing on the three-dimensional model to generate a high-precision model.

[0008] Optionally, the method of acquiring oblique image data by using a drone and performing aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data includes: performing oblique flight design to determine the flight altitude, overlap, route and image control points to obtain design information; based on the design information, performing oblique photography by using a drone to acquire image photos; extracting POS data based on the image photos; using the ground control point data in combination with the POS data to perform geometric correction and absolute orientation on the image photos to obtain the oblique image data; performing aerial triangulation calculation on the oblique image data to obtain the aerial triangulation encrypted point cloud data.

[0009] Optionally, the acquiring point cloud data by using a laser scanner includes: designing a driving station, determining a scanning range and a scanning station; and acquiring the point cloud data by using a laser scanner according to the scanning range and the scanning station.

[0010] Optionally, the preprocessing of the point cloud data after registering it in combination with the ground control point data to obtain laser point cloud data includes: registering the point cloud data in combination with the ground control point data; segmenting, denoising, and simplifying the registered point cloud data to obtain the laser point cloud data.

[0011] Optionally, the three-dimensional model is refined to generate a high-precision model, including: monomerizing the three-dimensional model to decompose a complex scene into independent monomer models; automatically texture mapping the monomer models based on the oblique image data to obtain a refined model; and Mesh modeling the refined model to obtain the high-precision model.

[0012] Optionally, the monomerizing the three-dimensional model to decompose the complex scene into independent monomer models includes: setting an elevation reference plane according to points on the top corners of the building model; constructing a monomerized model using building vector data, and constructing the main body of the building according to the oblique image data, the elevation reference plane and the building vector data; constructing the building's auxiliary structure according to the actual shape of the building and the thickness of the auxiliary structure in the oblique image data; and inspecting and modifying the building's white film to obtain the monomer model.

[0013] Optionally, the refined model is modified with a Mesh model to obtain the high-precision model, including: outlining the water area range line, adjusting the range space parameters, constructing surfaces according to the contours and mapping the textures; selecting the road surface range, leveling the road surface, automatically mapping the Mesh map and modifying the texture; checking and deleting suspended matter in the refined model to ensure that the model is neat and beautiful.

[0014] Based on the same inventive concept, the present invention also provides a real-scene three-dimensional model construction system, which includes: a control point data acquisition module, which is used to collect control point coordinates to obtain ground control point data; a laser scanner, which is used to collect point cloud data; a laser point cloud data processing module, which is used to pre-process the point cloud data after aligning it in combination with the ground control point data to obtain laser point cloud data; an oblique image data acquisition module, which is used to collect oblique image data; an oblique image data processing module, which is used to perform aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; a data fusion processing module, which is used to fuse and reconstruct the aerial triangulation encrypted point cloud data and the laser point cloud data to generate a three-dimensional model; and a model processing module, which is used to refine the three-dimensional model to generate a high-precision model.

[0015] Based on the same inventive concept, the present invention also provides a computer storage medium storing one or more programs, which can implement any of the aforementioned methods when the one or more programs are executed.

[0016] Based on the same inventive concept, the present invention also provides a device, including a processor, a communication interface, a memory and a communication bus; the processor, the communication interface and the memory communicate with each other through the communication bus; the processor is used to execute the program stored in the aforementioned computer-readable storage medium.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. The present invention can significantly improve the precision and accuracy of the three-dimensional model by comprehensively utilizing the basic point cloud data, ground control point data and image data collected by the drone, combined with aerial triangulation and laser point cloud processing technology; the layout of the ground control points serves as the basis for basic data processing, ensuring the accuracy of the data processing process, thereby generating a high-quality three-dimensional model;

[0019] 2. The method of the present invention realizes automated processing in multiple links, such as automatic texture mapping, Mesh model modification, etc., which reduces manual intervention and improves work efficiency. In addition, by optimizing the data processing process, such as the registration, segmentation, denoising and simplification of point cloud data, and the calculation of aerial triangulation, the speed of model construction is further accelerated, making the entire modeling process more efficient.

[0020] 3. The method of the present invention is not only applicable to the modeling of complex scenes, such as urban areas, natural landscapes, etc., but can also be processed individually according to specific needs, and complex scenes can be decomposed into independent monomer models. This flexibility makes the model more convenient in subsequent applications, such as splitting, merging or modifying the model as needed to adapt to more types of data sources and more complex modeling needs.

[0021] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 The present invention is a flowchart of a method for constructing a real-scene three-dimensional model according to an embodiment of the present invention.

[0024] Figure 2 It is a schematic diagram of the process execution of a method for constructing a real-scene three-dimensional model according to an embodiment of the present invention.

[0025] Figure 3 It is a structural schematic diagram of a real-scene three-dimensional model construction system according to an embodiment of the present invention.

[0026] Figure 4 It is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Reference Figure 1 An embodiment of the present invention proposes a method for constructing a real-scene three-dimensional model, which uses three-dimensional laser scanning technology to assist oblique photogrammetry, uses ground control points to unify coordinates, and repairs point clouds missing in blind areas, thereby ensuring the integrity of point clouds in obstructed areas of oblique photography and improving the precision of the model.

[0029] The method of this embodiment specifically includes:

[0030] Place control points in the area to be collected and collect the coordinates of the control points to obtain ground control point data;

[0031] Specifically, a series of ground control points are pre-deployed in the area, which will serve as the benchmark for subsequent data processing to improve the precision and accuracy of the data.

[0032] For example, an area to be surveyed is selected, such as an urban area, industrial park or mountainous area, and a certain number of ground control points are evenly distributed in the area according to the surveying and mapping accuracy requirements; these control points should have obvious identification, such as signboards or ground markings, so that drones and laser scanners can accurately identify and locate them; the precise coordinates of each control point (including longitude, latitude and elevation) are collected, for example, high-precision GNSS receivers or other measuring equipment can be used, and these coordinate data will be used as a benchmark for subsequent processing.

[0033] Obtain oblique image data using a drone, and perform aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data;

[0034] For example, a drone is used to take aerial photos of the park and obtain oblique image data. The ground control point data and the oblique image data taken by the drone are combined to perform aerial triangulation. This process includes steps such as feature point extraction, matching, and adjustment, and ultimately generates aerial triangulation encrypted point cloud data. This data not only contains the three-dimensional position information of the objects, but also reflects the texture characteristics of the objects.

[0035] Using a laser scanner to acquire point cloud data, and pre-processing the point cloud data after registering it with the ground control point data to obtain laser point cloud data;

[0036] Specifically, the laser scanner can be a terrestrial laser scanner (such as FARO Focus series, Z+F IMAGER series, Riegl VZ series, etc.) or a mobile laser scanner (such as NavVis M6 scanning cart, Trimble MX series, Velodyne HDL series, etc.); laser scanning is performed in the target area to obtain a large number of three-dimensional coordinate points on the surface of the object to form point cloud data, which will contain detailed information such as the shape, size and position of the object; choose a suitable terrestrial or mobile laser scanner to ensure its high accuracy and efficiency.

[0037] Exemplarily, the basic point cloud data acquired by the laser scanner is registered with the ground control point data; through coordinate transformation and error correction, the point cloud data can be accurately incorporated into the known control point coordinate system; the registered point cloud data is preprocessed, and the preprocessing may be denoising, simplification and segmentation, so as to remove noise points and redundant data, thereby obtaining laser point cloud data and improving the quality and availability of the point cloud data.

[0038] The aerial triangulation encrypted point cloud data and the laser point cloud data are fused and reconstructed to generate a three-dimensional model;

[0039] Specifically, the aerial triangulated encrypted point cloud data and the laser point cloud data are fused, and the two types of data are integrated into a unified point cloud data set through point cloud alignment and fusion algorithms. The fused point cloud data is used to reconstruct the model through 3D modeling software to generate a 3D model that will reflect the actual terrain and object distribution of the target area.

[0040] Exemplarily, the aerial triangulated encrypted point cloud data and the laser point cloud data are imported into professional 3D modeling software (such as Bentley ContextCapture, Autodesk ReCap, DJI Zhitu, etc.); these software usually have powerful data fusion functions, which can automatically or semi-automatically align and fuse the two point cloud data, and use the fused point cloud data to generate a 3D model. The 3D model can be model data in OSGB (OpenSceneGraph Binary) format or model data in OBJ (Object File Format) format. These model data are digital representations of the 3D scene and contain information such as vertices, faces, and textures.

[0041] The three-dimensional model is refined to generate a high-precision model.

[0042] Exemplarily, the model data in OSGB format or the model data in OBJ format are refined, including monomer processing, texture mapping, model modification, etc., and the model parameters are adjusted and texture information is added to make the model more realistic and accurate. After the refined processing, a final high-precision model is generated; the model will have the characteristics of high precision, high resolution and high realism, and can be used in many fields such as protected area supervision, urban and rural planning, emergency disaster relief, smart tourism, architectural design, and environmental monitoring.

[0043] Optionally, the obtaining of oblique image data by using a drone, and performing aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data includes:

[0044] Perform tilt flight design to determine the flight altitude, overlap, route and image control points to obtain design information;

[0045] For example, the appropriate altitude is calculated based on the camera parameters used for drone photography (such as focal length, sensor size), the required ground resolution (GSD, Ground Sampling Distance) and the terrain characteristics of the area; to ensure the quality of subsequent image stitching, it is necessary to set appropriate forward overlap (such as 80%) and lateral overlap (such as 60%), which means that there needs to be enough overlapping area between adjacent photos during the flight of the drone; use professional route planning software, such as Pix4Dcapture, DJI GS Pro, etc., to plan the optimal flight route based on information such as regional boundaries, terrain features, flight restrictions (such as no-fly zones), etc. The route should cover the entire target area and take into account weather factors such as wind direction and wind speed; a certain number of ground control points (GCP, Ground Control Points) are reasonably arranged in the target area. These points should have obvious characteristics and be easy to identify in the image for subsequent geometric correction and absolute orientation.

[0046] According to the design information, using a drone to perform oblique photography to obtain image photos;

[0047] For example, according to a designed flight plan, a drone can be equipped with a multi-lens tilt camera for flight photography; the drone flies along a preset route, and the multi-lens camera takes photos from multiple angles (such as front, back, left, right, and bottom) to obtain rich three-dimensional information.

[0048] Extracting POS data based on the image photo;

[0049] For example, during the flight, the drone will record the shooting position (longitude, latitude, altitude) and attitude (pitch angle, roll angle, yaw angle) information of each photo. This information is called POS (Position and Orientation System) data, and the POS data can be extracted through processing.

[0050] Using the ground control point data and combining it with the POS data, the image photo is geometrically corrected and absolutely oriented to obtain the oblique image data;

[0051] Exemplarily, the extracted POS data is combined with the ground control point (GCP) data, and professional photogrammetry software (such as Pix4Dmapper, Agisoft Metashape, etc.) is used to perform geometric correction and absolute orientation on the image photos to obtain oblique image data. This step aims to eliminate errors such as camera distortion and atmospheric effects, and accurately position the image photos in the actual geographic coordinate system.

[0052] The oblique image data is subjected to aerial triangulation calculation to obtain the aerial triangulation encrypted point cloud data.

[0053] For example, after geometric correction and absolute orientation, the image photos are calculated through aerial triangulation (AT), using the overlapping areas between the images and the known POS data to calculate more three-dimensional point coordinates (i.e., aerial triangulation encrypted points); these points constitute the three-dimensional point cloud data of the target area, reflecting the surface morphology and building structure. At the same time, the data is combined with the ground control point data, so that the aerial triangulation encrypted point cloud data and the laser point cloud data are in the same coordinate system.

[0054] Optionally, the acquiring point cloud data by using a laser scanner includes:

[0055] Design driving sites and determine scanning range and scanning sites;

[0056] Specifically, it is necessary to conduct a detailed on-site survey of the target area to understand the topography, building distribution, traffic conditions, etc., in order to determine the best scanning site; according to the task requirements, clarify the specific area to be scanned, and draw a corresponding map or sketch; clearly mark the area to be scanned on the map, and consider the ambiguity and overlap of the boundaries to ensure the integrity and accuracy of the data; if there are large elevation changes in the target area (such as mountains, hills, etc.), it is also necessary to consider setting up scanning sites at different heights to obtain more comprehensive terrain information; mark the specific coordinates of each scanning site on the map, and record the relevant information of each site (such as height, direction, etc.); when setting up scanning sites on the spot, you can use obvious markers (such as flags, reflectors, etc.) to mark the site location so that it can be accurately identified and located.

[0057] For example, a reasonable number of scanning stations is determined according to the size, shape and complexity of the target area; generally speaking, the number of stations should be sufficient to cover the entire area and ensure that each station can obtain clear and complete point cloud data; for example, in a complex urban park, multiple stations may need to be set up to ensure that data at different angles and heights can be effectively collected; the height and distance of the station should be determined according to the scanning range; usually the station is set at a relatively high position to obtain a wider field of view and more detailed point cloud data; at the same time, the distance between stations should also be moderate to ensure that the data between adjacent stations can be smoothly spliced.

[0058] The point cloud data is acquired using a laser scanner according to the scanning range and the scanning site.

[0059] Specifically, select a suitable laser scanner and check its working status, ensure that the battery is fully charged and the storage space is sufficient, and prepare the necessary accessories and tools; set the scanning parameters of the laser scanner according to the characteristics and accuracy requirements of the scanning area. This includes scanning speed, resolution, scanning angle, etc.; start the laser scanner at the predetermined scanning site and scan according to the preset scanning parameters. During the scanning process, it is necessary to ensure the stability of the scanner and avoid interference from obstructions and reflective objects; after the scanning is completed, it is necessary to conduct a preliminary check on the acquired point cloud data; check whether the data is complete and clear, and whether there is obvious noise or outliers.

[0060] Exemplarily, after determining the scanning site, a terrestrial laser scanner (such as the FARO Focus series) can be used for scanning; the scanner is placed on the predetermined site, and preliminary calibration and parameter settings are performed, and then the scanner is started and scanned according to the preset scanning path and parameters; during the scanning process, the status of the scanner and the scanning results are monitored; if it is found that obstructions or reflectors affect the scanning quality, the scanning site or scanning parameters are adjusted.

[0061] Optionally, the step of registering the point cloud data in combination with the ground control point data and performing preprocessing to obtain the laser point cloud data includes:

[0062] In combination with the ground control point data, the point cloud data is registered;

[0063] Specifically, since the point cloud data obtained from each scan is based on its own coordinate system when scanning at multiple positions or angles, it is necessary to combine the ground control point data and convert these point cloud data into a unified coordinate system through registration to achieve seamless data stitching.

[0064] Exemplarily, the point cloud data and ground control point data are imported into point cloud processing software, such as CloudCompare, PDAL or PCL (Point Cloud Library), etc., and the ground control points are matched with the corresponding points in the point cloud obtained by the actual scan. This usually involves selecting points in the point cloud data that are close to the ground control points, and accurately matching them through certain algorithms (such as nearest neighbor search); after the matching is completed, these matching point pairs can be used to calculate a coordinate transformation matrix, which describes the transformation relationship from the local coordinate system of the scanner to the global coordinate system (i.e., the coordinate system where the ground control points are located); this coordinate transformation matrix is ​​applied to the entire basic point cloud data to achieve point cloud registration; the registered point cloud data will have the same coordinate system as the ground control points, so that the data maintains consistency and accuracy on a global scale.

[0065] The registered point cloud data is segmented, denoised, and simplified to obtain the laser point cloud data.

[0066] Specifically, when acquiring point cloud data, it will be affected by the target object and the scanning range, thereby collecting a large amount of redundant point cloud data. This data will take up instrument storage space and affect the efficiency of point cloud data calculation and processing. Therefore, it is necessary to segment the point cloud data and extract the data required for the target object; since the scanning process may be affected by factors such as environmental noise and equipment errors, the point cloud data will contain some abnormal points or noise points, which will affect the accuracy of subsequent modeling, so denoising is required; the original point cloud data is often very large and contains a lot of redundant information. In order to improve the speed and efficiency of subsequent processing, the point cloud data needs to be streamlined to remove unnecessary points while maintaining the geometric characteristics and details of the data.

[0067] Exemplarily, the point cloud data is segmented into different parts or objects for subsequent processing, for example, this can be achieved through a segmentation algorithm based on geometric features (such as planes, surfaces, etc.); noise points in the point cloud data are removed, which are usually caused by errors in the scanning process or environmental factors (such as leaf jitter, water surface reflection, etc.), and denoising can be achieved through statistical filtering, radius filtering and other methods; the number of points in the point cloud data is reduced to reduce the data volume and increase the processing speed, for example, this can be achieved through voxel grid downsampling, random sampling and other methods; after obtaining the aligned building point cloud data, a plane-based segmentation algorithm is used to separate the ground from the main body of the building, and then the point cloud data of the main body of the building is denoised to remove noise points caused by leaf occlusion, pedestrians passing by, etc. Finally, in order to reduce the data volume and improve the efficiency of subsequent modeling, the point cloud data is streamlined using a voxel grid downsampling method, and the points in each voxel are merged into a representative point to obtain the final laser point cloud data.

[0068] Optionally, the performing fine processing on the three-dimensional model to generate a high-precision model includes:

[0069] Performing monomer processing on the three-dimensional model to decompose the complex scene into independent monomer models;

[0070] Exemplarily, independent monomer models (such as each building, each bridge, etc.) are identified and separated from the model data in OSGB format or the model data in OBJ format, thereby obtaining a plurality of independent monomer models, each of which represents a specific object in the scene.

[0071] Automatically texture mapping the monomer model based on the oblique image data to obtain a refined model;

[0072] For example, high-resolution texture information in oblique image data is used to automatically perform texture mapping on a single model; this usually involves matching pixels in the image with vertices on the model surface and "pasting" the image's texture information onto the model. The single model is given a real texture and looks more realistic.

[0073] The refined model is subjected to Mesh model modification to obtain the high-precision model.

[0074] Exemplarily, the refined model after automatic texture mapping is further modified and optimized; this may include removing noise, smoothing surfaces, adjusting texture details, etc.; a finely modified Mesh model is obtained, whose geometry and texture are closer to the real world, and the modified Mesh model is exported to the required format (such as FBX, DAE, etc.), and used as a high-precision model for subsequent applications, such as virtual reality, augmented reality, urban planning, etc.

[0075] Optionally, the performing monomer processing on the three-dimensional model to decompose the complex scene into independent monomer models includes:

[0076] Set the elevation reference plane based on the points at the top corners of the building model;

[0077] For example, suppose you are dealing with a complex urban area containing multiple high-rise buildings. First, you need to set an elevation reference for each building model. This is usually done by identifying points on the top corners of the building model (such as the four corner points of the roof); by selecting these points and building a horizontal plane based on them as the elevation reference of the building, this reference plane will be used to determine its height and position when subsequently constructing the main body of the building.

[0078] Using the building vector data to construct a monomer model, and constructing the main body of the house according to the oblique image data, the elevation reference plane and the building vector data;

[0079] Exemplarily, vector data of a building (such as CAD drawings, GIS data, etc.) are used to construct the framework of a monomeric model; these vector data usually include information such as the building's outline, number of floors, and locations of doors and windows; these vector data are imported, and a three-dimensional framework of the building is constructed based on the vector data; then, the main body of the house is constructed by combining oblique image data and an elevation datum; the oblique image data provides detailed texture and shape information of the building's exterior, while the elevation datum ensures the accuracy of the building in the vertical direction; by combining these data with the building's vector data, a main body of the house with a realistic appearance and accurate size is constructed.

[0080] Constructing the auxiliary structure of the building according to the actual shape of the building and the thickness of the auxiliary structure in the oblique image data;

[0081] For example, buildings usually contain many auxiliary structures, such as balconies, awnings, air conditioner units, etc. These structures may not be clear or complete in oblique image data, so it is necessary to estimate their size and position based on the actual shape of the building and clues in the oblique image data; for example, the 3D model of the balcony can be constructed based on the width and depth of the balcony in the image, as well as the spacing between adjacent floors. Similarly, we can also use similar methods to construct other auxiliary structures.

[0082] The building white membrane is inspected and modified to obtain the monomer model.

[0083] For example, after building a single model, it needs to be checked and modified; this process is often called a "white film check" because it is performed without texture mapping; check whether the model's geometry is accurate, whether there are problems such as broken surfaces or overlaps; if problems are found, you can use the tools in the 3D modeling software to repair and adjust them; for example, we can use the "smooth" tool to eliminate sharp corners, or use the "cut" tool to correct incorrect shapes.

[0084] Optionally, performing Mesh model modification on the refined model to obtain the high-precision model includes:

[0085] Outline the water area, adjust the range space parameters, construct the surface according to the outline and map the texture;

[0086] For example, assuming that the refined model includes a lake or a river, it is necessary to use tools in the 3D modeling software (such as polygon drawing tools) to outline the exact range lines of the water area; these lines should fit closely to the boundaries of the water area to ensure that the constructed water surface area is accurate; adjust the range space parameters, such as the height, depth, slope, etc. of the water surface to simulate the real water environment. The settings of these parameters will affect the appearance and dynamic effects of the water surface; construct meshes (Mesh) of the water surface area according to the outlined contours. These meshes will form the three-dimensional surface of the water area. During the construction process, it is necessary to pay attention to the density and distribution of the meshes to ensure the smoothness and detail of the water surface; texture mapping is applied to the water surface, which usually involves selecting a suitable water surface texture map (such as ripples, reflections, etc.) and applying it to the constructed water surface area. By adjusting parameters such as texture scaling, offset and blending mode, a realistic water surface effect can be achieved.

[0087] Select the road surface range, perform road leveling, automatic mesh mapping and texture modification;

[0088] For example, in a city or road scene, the selection tools in the 3D modeling software are used to select the road surface area, which usually involves cutting or masking the model to separate the road surface part; the road surface is flattened, which means that we need to adjust the height and slope of the road surface to make it look flat and consistent with the actual terrain. The flattening process helps to eliminate the small ups and downs and unevenness caused by the model building process; the road surface texture is applied to the road surface using the Mesh map automatic mapping function, which usually involves selecting a suitable road surface texture map (such as asphalt, concrete, etc.) and setting the coordinates and size of the map and other parameters; the road surface texture is modified, which includes adjusting the texture's brightness, contrast, saturation and other parameters, and adding detail elements (such as cracks, oil stains, etc.) to enhance the realism of the road surface.

[0089] Check and delete the suspended objects in the refined model to ensure that the model is neat and beautiful.

[0090] For example, due to image matching, occlusion by surrounding buildings or the presence of appendages, 3D models will form 3D fragments, or suspended objects, in the air or underground. At this time, adjust to the free view state to view the suspended objects in the model, select and delete them in batches. For particularly small fragments or fragments connected to buildings, you need to adjust different viewing angles, select them locally and delete them. Deleting suspended objects can make the model more beautiful.

[0091] In summary, if Figure 2 As shown in the figure, a series of ground control points are pre-deployed in the target area, and the ground control point data are collected as the basis for subsequent data processing; the pilot station design is carried out to determine the scanning range and scanning station, and the laser scanner is used to scan and obtain point cloud data; the point cloud data is registered based on the ground control point data, and segmented, denoised, and simplified to obtain laser point cloud data; the inclined flight design is carried out to determine the flight altitude, overlap, route and image control points, and the UAV is used for inclined photography to obtain image photos and extract POS data; the image photos are geometrically corrected and absolutely oriented in combination with the ground control point data and POS data to obtain inclined image data, and then aerial triangulation calculation is performed. , generate aerial triangulation encrypted point cloud data; fuse the aerial triangulation encrypted point cloud data with the laser point cloud data to generate initial model data, which can be model data in OSGB format or model data in OBJ format; decompose the complex scenes in the model data in OSGB format or model data in OBJ format into independent monomer models, and construct the monomer models by setting the elevation reference surface and using the building vector data; perform automatic texture mapping on the monomer models based on the oblique image data to generate a refined model; perform Mesh model modification on the refined model, including outlining the water area boundary line, adjusting the road surface, deleting suspended matter and other steps to ensure that the model is neat and beautiful, thereby generating a high-precision model.

[0092] Based on the same inventive concept, Figure 3 As shown, the present invention also provides a real-scene three-dimensional model construction system, the system comprising:

[0093] The control point data acquisition module is used to collect the coordinates of the control points and obtain the ground control point data;

[0094] Laser scanner, used to collect point cloud data;

[0095] A laser point cloud data processing module, used to pre-process the point cloud data after registering it in combination with the ground control point data to obtain laser point cloud data;

[0096] An oblique image data acquisition module, used for acquiring oblique image data;

[0097] The oblique image data processing module is used to perform aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data;

[0098] A data fusion processing module, used for fusing and reconstructing the aerial triangulation encrypted point cloud data and the laser point cloud data to generate a three-dimensional model;

[0099] The model processing module is used to perform fine processing on the three-dimensional model to generate a high-precision model.

[0100] Based on the above disclosed content, the present invention also provides an electronic device. Figure 4 As shown, the electronic device of an embodiment of the present invention includes at least one electrically connected processor and at least one storage medium, wherein the storage medium is electrically connected to the processor, wherein the storage medium stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0101] Based on the same inventive concept, the present invention also provides a storage medium, which stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method as described above.

[0102] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved.

[0103] The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention. That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the technical field not recorded in the present invention.

Claims

1. A method for constructing a real-scene three-dimensional model, characterized in that: The method comprises: Place control points in the area to be collected and collect the coordinates of the control points to obtain ground control point data; Obtain oblique image data using a drone, and perform aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; Using a laser scanner to acquire point cloud data, and pre-processing the point cloud data after registering it with the ground control point data to obtain laser point cloud data; The aerial triangulation encrypted point cloud data and the laser point cloud data are fused and reconstructed to generate a three-dimensional model; The three-dimensional model is refined to generate a high-precision model.

2. A method for constructing a real-scene three-dimensional model according to claim 1, characterized in that: The method of using a drone to obtain oblique image data and combining the ground control point data to perform aerial triangulation calculation on the oblique image data to obtain aerial triangulation encrypted point cloud data includes: Perform tilt flight design to determine the flight altitude, overlap, route and image control points to obtain design information; According to the design information, using a drone to perform oblique photography to obtain image photos; Extracting POS data based on the image photo; Using the ground control point data and combining it with the POS data, the image photo is geometrically corrected and absolutely oriented to obtain the oblique image data; The oblique image data is subjected to aerial triangulation calculation to obtain the aerial triangulation encrypted point cloud data.

3. A method for constructing a real-scene three-dimensional model according to claim 1, characterized in that: The method of obtaining point cloud data by using a laser scanner comprises: Design driving sites and determine scanning range and scanning sites; The point cloud data is acquired using a laser scanner according to the scanning range and the scanning site.

4. A method for constructing a real-scene three-dimensional model according to claim 1, characterized in that: The point cloud data is pre-processed after being registered in combination with the ground control point data to obtain the laser point cloud data, including: In combination with the ground control point data, the point cloud data is registered; The registered point cloud data is segmented, denoised, and simplified to obtain the laser point cloud data.

5. A method for constructing a real-scene three-dimensional model according to claim 2, characterized in that: The method further comprises: Performing monomer processing on the three-dimensional model to decompose the complex scene into independent monomer models; Automatically texture mapping the monomer model based on the oblique image data to obtain a refined model; The refined model is subjected to Mesh model modification to obtain the high-precision model.

6. A method for constructing a real-scene three-dimensional model according to claim 5, characterized in that: The method further comprises: Set the elevation reference plane based on the points at the top corners of the building model; Using the building vector data to construct a monomer model, and constructing the main body of the house according to the oblique image data, the elevation reference plane and the building vector data; Constructing the auxiliary structure of the building according to the actual shape of the building and the thickness of the auxiliary structure in the oblique image data; The building white membrane is inspected and modified to obtain the monomer model.

7. A method for constructing a real-scene three-dimensional model according to claim 5, characterized in that: The Mesh model modification of the refined model to obtain the high-precision model includes: Outline the water area, adjust the range space parameters, construct the surface according to the outline and map the texture; Select the road surface range, perform road leveling, automatic mesh mapping and texture modification; Check and delete the suspended objects in the refined model to ensure that the model is neat and beautiful.

8. A real-scene three-dimensional model construction system, characterized in that: The system comprises: The control point data acquisition module is used to collect the coordinates of the control points and obtain the ground control point data; Laser scanner, used to collect point cloud data; A laser point cloud data processing module, used to pre-process the point cloud data after registering it in combination with the ground control point data to obtain laser point cloud data; An oblique image data acquisition module, used for acquiring oblique image data; The oblique image data processing module is used to perform aerial triangulation calculation on the oblique image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; A data fusion processing module, used for fusing and reconstructing the aerial triangulation encrypted point cloud data and the laser point cloud data to generate a three-dimensional model; The model processing module is used to perform fine processing on the three-dimensional model to generate a high-precision model.

9. A computer storage medium, characterized in that: One or more programs are stored, and when the one or more programs are executed, a real-scene three-dimensional model construction method as described in any one of claims 1-7 can be implemented.

10. A device, characterized in that: The system comprises a processor, a communication interface, a memory and a communication bus; the memory stores at least one program in a computer storage medium as claimed in claim 9 which can be loaded and executed by the processor.

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