Modeling method and system for fusing live-action three-dimensional data and digital model

Abstract

The invention provides a modeling method and system for fusing live-action three-dimensional data and a digital model, and the method comprises the steps: obtaining real-time point cloud data in a preset terrain scene, taking the real-time point cloud data as the live-action three-dimensional data, and loading a digital model as a global terrain reference; carrying out optimization processing on the live-action three-dimensional data, and carrying out space-time alignment on the optimized live-action three-dimensional data and the digital model to generate an alignment data set; and determining a final fusion weight of the aligned live-action three-dimensional data and the aligned digital model based on the spatial distribution density and the terrain curvature value of the aligned data set so as to perform fusion processing on the aligned live-action three-dimensional data and the aligned digital model to generate a three-dimensional terrain model. Modeling of fusion of live-action three-dimensional data and a digital model is realized; according to the method, the problems of non-uniform space-time reference of multi-source data, surface distortion of a fusion model and detail loss in a complex terrain scene are solved, and high-precision and high-integrity live-action three-dimensional and terrain reference data fusion modeling is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and in particular to a modeling method and system for fusing real-scene three-dimensional data with a digital model. Background Art

[0002] The demand for accurate terrain models is increasing in fields such as modern urban planning, environmental protection, and disaster prevention. These applications require models that not only accurately reflect the detailed features of the terrain, but also possess high accuracy and the ability to update in real time to facilitate timely response to environmental changes and make precise decisions. For example, in urban expansion projects, designers need to use the latest terrain data to plan the optimal location and height of buildings to avoid potential geological risks. Similarly, in forest resource management and conservation, understanding subtle changes in the surface helps more effectively monitor forest health and biodiversity.

[0003] The current mainstream solution adopts a static registration and fixed weight fusion method based on feature matching. It rigidly registers real-life 3D data with digital models through an iterative nearest point algorithm, and achieves spatial alignment using scale-invariant feature point matching; the registered data is weighted and fused according to preset fixed weights; this method relies on high-precision feature extraction and manual experience to set weights, and has the following significant defects: static registration cannot adapt to dynamic terrain changes, and the iterative nearest point algorithm assumes that the terrain geometry is fixed, resulting in accumulated registration errors in dynamic areas such as landslides and subsidence; secondly, the fixed weight allocation strategy is out of line with the complexity of the terrain, and is prone to surface distortion of the fused model in high-curvature areas such as steep slopes and building edges; thirdly, feature matching relies on data integrity, and the registration accuracy is greatly reduced when point clouds are missing in shadow areas. Summary of the Invention

[0004] The present invention provides a modeling method and system for fusing real-life 3D data with digital models, aiming to address the problems in the prior art where static registration cannot adapt to dynamic terrain changes, resulting in accumulated registration errors in dynamic areas such as landslides and subsidence; the fixed weight distribution strategy is out of sync with the terrain, easily causing surface distortion of the fused model in areas such as steep slopes and building edges; and the registration accuracy is greatly reduced when point clouds are missing in shadow areas.

[0005] In a first aspect, the present invention provides a modeling method for fusing real-scene three-dimensional data with a digital model, comprising: Acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model; Optimizing the real-scene three-dimensional data to generate optimized real-scene three-dimensional data; Performing spatiotemporal alignment on the optimized real-scene three-dimensional data and the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene three-dimensional data and the aligned digital model; Determining a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; According to the final fusion weight, the aligned real scene 3D data and the aligned digital model are fused to generate a 3D terrain model, so as to achieve modeling by fusion of the real scene 3D data and the digital model.

[0006] Optionally, optimizing the real-scene three-dimensional data to generate optimized real-scene three-dimensional data includes: Based on the spatial distribution characteristics of the real-time point cloud data, phase compensation is performed on the three-dimensional points in the real-scene three-dimensional data that are disturbed by atmospheric scattering to obtain compensated three-dimensional points; Identifying a target shadow area in the real-scene 3D data, and reconstructing missing 3D points in the target shadow area by spatial interpolation based on elevation change trends of 3D points in adjacent non-shadow areas to generate virtual 3D points; The compensated three-dimensional points and the virtual three-dimensional points are merged to generate optimized real-scene three-dimensional data, and the point cloud coverage of the optimized real-scene three-dimensional data reaches a preset coverage value.

[0007] Optionally, identifying a target shadow area in the real-scene 3D data, and performing spatial interpolation reconstruction on missing 3D points in the target shadow area according to an elevation change trend of 3D points in adjacent non-shadow areas to generate virtual 3D points includes: Marking a closed area in the real-scene three-dimensional data where the point cloud coverage is lower than a preset shadow threshold as an initial shadow area, and extracting a first boundary point of the initial shadow area; Expanding the first boundary points according to the horizontal distribution range of the first boundary points and a preset buffer distance to generate a target shadow area; Selecting at least three directional reference points from the second boundary points of the target shadow area, and extracting three-dimensional points of adjacent non-shadow areas along the terrain extension direction of the directional reference points; Fitting and generating a plurality of terrain trend lines according to the elevation change trends of the three-dimensional points in the adjacent non-shadow areas; The slope of the terrain trend line is used as the first elevation change rate of the virtual three-dimensional point, and the elevation value of the virtual three-dimensional point is calculated along the extension direction of the terrain according to a preset step size. The virtual three-dimensional point is generated by combining the first elevation change rate and the elevation value of the virtual three-dimensional point.

[0008] Optionally, determining a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set includes: Dividing the aligned data set into a plurality of geographic units according to a preset size; Counting the number of point clouds of the aligned real-scene three-dimensional data within each geographic unit, calculating a ratio of the number of point clouds to the area of the geographic unit, and using the ratio as a spatial distribution density value; Extracting elevation values of all center points from the aligned digital models in the geographic unit to calculate a second elevation change rate between the center point and the center points of adjacent geographic units; Calculating a terrain curvature value of the geographic unit according to the second elevation change rate; Determining a fusion weight to be verified between the aligned real-scene three-dimensional data and the aligned digital model according to the spatial distribution density value and the terrain curvature value; Verify whether the fusion weight to be verified is within a preset range. If not, repeatedly divide the aligned dataset into multiple geographic units according to a preset size until a predetermined convergence state is reached, complete the iterative update, and obtain the final fusion weight. The predetermined convergence state is that the final fusion weight is within the preset range.

[0009] Optionally, the optimized real-scene 3D data is temporally and spatially aligned with the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene 3D data and the aligned digital model, including: Extracting the three-dimensional coordinates of the geographic control points in the optimized real-scene three-dimensional data and the elevation reference values of the corresponding geographic locations in the digital model; Calculating spatial position conversion parameters of the optimized real-scene 3D data relative to the digital model based on the difference between the 3D coordinates of the geographic control point and the elevation reference value, wherein the spatial position conversion parameters include translation parameters, rotation parameters, and scaling parameters; Based on the spatial position conversion parameters, coordinate conversion is performed on all three-dimensional points of the optimized real-scene three-dimensional data to generate converted real-scene three-dimensional data; Performing time synchronization processing on the converted real-scene three-dimensional data and the digital model to obtain synchronized data; The converted real-scene three-dimensional data and the synchronized data are merged into an aligned data set.

[0010] Optionally, according to the difference between the three-dimensional coordinates of the geographic control point and the elevation reference value, the spatial position conversion parameters of the optimized real scene three-dimensional data relative to the digital model are calculated, and the spatial position conversion parameters include translation parameters, rotation parameters and scaling parameters, including: Calculating the difference between the vertical coordinate of the geographic control point and the corresponding elevation reference value to generate a translation parameter to be verified according to the difference; Calculating the azimuth deviation between the horizontal coordinates of the geographic control point and the plane coordinates corresponding to the digital model to generate an average rotation angle, converting the average rotation angle into a horizontal direction correction value, and using the horizontal direction correction value as a rotation parameter to be verified; selecting at least two pairs of geographic control points, calculating an actual distance between each pair of geographic control points in the optimized real-life three-dimensional data and a theoretical distance between corresponding geographic control points in the digital model, and generating a scaling parameter to be verified based on the actual distance and the theoretical distance; The translation parameter to be verified, the rotation parameter to be verified, and the scaling parameter to be verified are verified to generate spatial position conversion parameters that meet a preset tolerance condition.

[0011] Optionally, the aligned real-scene three-dimensional data and the aligned digital model are fused according to the final fusion weight to generate a three-dimensional terrain model, including: Extracting the elevation values of all 3D points from the aligned real-scene 3D data in the geographic unit, and calculating the real-scene elevation contribution value by combining the final fusion weights; Calculating a base elevation contribution value based on the elevation values of all center points in the aligned digital model in the geographic unit and the final fusion weight; Combining the actual elevation contribution value and the reference elevation contribution value, calculating the fused elevation value corresponding to each geographic unit to construct an elevation surface covering the preset terrain scene; The elevation surface is bound to the surface reflection properties of the aligned real-scene three-dimensional data to generate a three-dimensional terrain model.

[0012] In a second aspect, the present invention provides a modeling system for integrating real-scene three-dimensional data with a digital model, comprising: An acquisition module is used to acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model; An optimization module, configured to optimize the real-scene three-dimensional data to generate optimized real-scene three-dimensional data; an alignment module, configured to perform spatiotemporal alignment of the optimized real-scene 3D data with the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene 3D data and the aligned digital model; a determination module, configured to determine a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; A generation module is used to fuse the aligned real-scene 3D data with the aligned digital model according to the final fusion weight to generate a 3D terrain model, so as to achieve modeling of the fusion of the real-scene 3D data and the digital model.

[0013] In a third aspect, the present invention provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a modeling method for fusing real-scene three-dimensional data with a digital model as described in any one of the first aspects.

[0014] In a fourth aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a modeling method for fusing real-scene three-dimensional data with a digital model as described in any one of the first aspects.

[0015] In the present invention, real-time point cloud data in a preset terrain scene is obtained, the real-time point cloud data is used as real-scene three-dimensional data, and a digital model is loaded as a global terrain benchmark, wherein the digital model includes: a digital elevation model and / or a digital surface model; the real-scene three-dimensional data is optimized to generate optimized real-scene three-dimensional data; the optimized real-scene three-dimensional data and the digital model are temporally and spatially aligned to generate an aligned data set, the aligned data set including the aligned real-scene three-dimensional data and the aligned digital model; based on the spatial distribution density and terrain curvature value of the aligned data set, the final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model is determined; according to the final fusion weight, the aligned real-scene three-dimensional data and the aligned digital model are fused to generate a three-dimensional terrain model, so as to realize modeling of the fusion of real-scene three-dimensional data and digital model. The technical solution provided by the present invention realizes multi-source collaboration between real-time point cloud and historical terrain benchmark data, solves the problem of incomplete coverage of a single data source, and provides global benchmark support for dynamic scene modeling; for point cloud loss and distortion caused by atmospheric noise and multipath effects, phase compensation and shadow repair are used to improve data integrity and suppress local detail distortion; spatiotemporal alignment realizes the synchronization of the coordinate system and time series of real-life data and digital models, and solves the error accumulation problem of static alignment; through the dynamic allocation of weights by the density-curvature dual factors, the contribution of real-life data is enhanced in high-detail areas, and the global consistency of the digital model is retained in smooth areas, avoiding surface mutations of the model caused by fixed weights; combined with geometric accuracy and surface continuity optimization, a three-dimensional model with both real-life details and terrain benchmark features is output, supporting millimeter-level deformation monitoring and dynamic updating. Furthermore, by performing phase compensation on the three-dimensional points in the real-scene three-dimensional data that are disturbed by atmospheric scattering, noise interference is suppressed; low-coverage shadow areas are identified, and the elevation change trend of the point cloud in the adjacent non-shadow area is extracted based on the extension direction of the terrain, and virtual three-dimensional points are generated to fill the data gaps. The data integrity is improved by merging the compensation points and the virtual points; the point cloud coverage in the shadow area is improved, and the local data distortion caused by atmospheric noise and multipath effects is effectively suppressed, breaking through the bottleneck of model breakage and accuracy reduction caused by data missing and noise accumulation in traditional methods.

[0016] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0018] Figure 1 A flow chart of a modeling method for fusing real-scene 3D data with a digital model provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a modeling system for fusing real-scene 3D data with a digital model provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Figure 1 The present invention provides a flowchart of a modeling method for fusing real-scene 3D data with a digital model. Figure 1 As shown, the method includes: Aiming at the problem of accuracy and timeliness in the fusion modeling of real-life three-dimensional data and digital models in complex terrain dynamic monitoring scenarios (such as landslides and urban underground space modeling), traditional solutions rely on static registration and fixed weight allocation, resulting in core defects such as dynamic terrain deformation error accumulation, model distortion in high curvature areas, and missing shadow data. The present invention proposes a technical path of dynamic spatiotemporal alignment-adaptive weight fusion: improving data integrity through real-time point cloud noise compensation and intelligent interpolation and repair of shadow areas; combining the spatiotemporal window matching mechanism to align multi-source data to solve the problem of inconsistent spatiotemporal benchmarks in dynamic terrain; dynamically allocating fusion weights based on the density-curvature dual factor, strengthening the contribution of real-life data in high-detail areas, and retaining the global features of digital models in smooth areas, breaking through the bottleneck of model surface mutation and accuracy attenuation caused by static weights and rigid registration of traditional methods, and realizing dynamic modeling and updating of three-dimensional terrain with millimeter-level accuracy in complex scenarios. Based on this, the present invention provides a modeling method for the fusion of real-life three-dimensional data and digital models, such as Figure 1 ,include: Step 101: Acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene 3D data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model.

[0023] In this step, the preset terrain scene refers to the area to be modeled with complex surface features (such as mountains and urban canyons). Real-time point cloud data refers to a dataset of surface and obstacle surfaces composed of three-dimensional coordinate points collected in real time by lidar. Real-scene three-dimensional data refers to point cloud data containing surface geometry, reflectivity and dynamic change information. Digital model refers to a model including: digital elevation model and / or digital surface model, where the digital elevation model (DEM) represents the surface elevation and the digital surface model (DSM) represents the height of the surface and surface cover (such as buildings and vegetation). Global terrain benchmark refers to a basic dataset used to provide global spatial reference and historical terrain features. It serves as a reference framework for spatial alignment of real-scene three-dimensional data and provides global terrain feature constraints in fusion modeling.

[0024] In an embodiment of the present invention, when acquiring real-time point cloud data in a preset terrain scene, the surface and obstacle surfaces of the preset terrain scene (such as mountains or urban canyons) are scanned by a lidar to generate a real-time point cloud dataset consisting of three-dimensional coordinate points, and the dataset is defined as real-scene three-dimensional data; at the same time, the historical digital elevation model and digital surface model data of the preset terrain scene are loaded from an existing database as global terrain reference data; the real-time point cloud data includes the geometry and reflectivity information of the terrain surface, and the digital model includes the elevation of the preset terrain scene and the height information of the surface cover.

[0025] Step 102: Optimize the real-scene 3D data to generate optimized real-scene 3D data.

[0026] In this step, the optimized real-scene 3D data refers to high-precision point cloud data after noise suppression and shadow restoration.

[0027] In an embodiment of the present invention, when optimizing the real-scene three-dimensional data, adaptive optical compensation technology is used to modulate the optical phase of the lidar. Specifically, the phase parameters of the laser transmitter are adjusted to compensate for signal attenuation and sensor noise interference caused by atmospheric scattering. At the same time, according to the multipath effect model, the missing point cloud in the target shadow area is spatially interpolated and repaired to generate optimized real-scene three-dimensional data, wherein the position error of each three-dimensional coordinate point is reduced and the data integrity of the shadow area is improved.

[0028] Step 103: performing spatiotemporal alignment on the optimized real-scene 3D data and the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene 3D data and the aligned digital model.

[0029] In this step, the aligned dataset refers to the matching dataset of the real-scene 3D data and the digital model after the spatiotemporal coordinate systems are synchronized.

[0030] In an embodiment of the present invention, when the optimized real-scene three-dimensional data and the digital model are temporally and spatially aligned, the local coordinate system of the optimized real-scene three-dimensional data is mapped to the global coordinate system of the digital model through a coordinate transformation algorithm, specifically including: calculating the elevation difference between the ground control points in the optimized real-scene three-dimensional data and the corresponding points in the digital model, and fitting the spatial position conversion parameters through the least squares method; at the same time, time synchronization correction is performed on the terrain change area according to the timestamp of the digital model to generate an aligned data set, wherein the spatial position error between the aligned real-scene three-dimensional data and the aligned digital model is less than a preset threshold.

[0031] Step 104: Determine a final fusion weight of the aligned real-scene 3D data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set.

[0032] In this step, spatial distribution density refers to the number of point clouds per unit area of the aligned real-world 3D data. Terrain curvature refers to the degree of surface curvature calculated using the second-order derivative of elevation. The final fusion weight is the proportional coefficient (0–1) between the contribution of the aligned real-world 3D data and the aligned digital model during the fusion process, including the verified fusion weight for each geographic unit.

[0033] In an embodiment of the present invention, the following process is specifically performed: first, the aligned data set is divided into multiple geographic units, each geographic unit containing aligned real-life 3D point cloud data and its corresponding aligned digital model; then, the spatial distribution density value of the aligned real-life 3D point cloud within each geographic unit is calculated, and at the same time, the terrain curvature value of the grid center point is calculated using the second-order difference method based on the aligned digital model; then, a density-curvature feature matrix is established, wherein the matrix rows correspond to the grid spatial positions, and the columns contain spatial distribution density values and terrain curvature values; finally, the spatial distribution density value and the terrain curvature value are added according to a preset ratio (such as the spatial distribution density value multiplied by 0.6 plus the terrain curvature value multiplied by 0.4), and the fusion weight to be verified corresponding to each geographic unit is calculated. After verification, the final fusion weight is output, wherein the first coefficient and the second coefficient are adaptively adjusted according to the terrain type. The final fusion weight ensures that the detailed features of the real-life 3D data are preferentially retained in high-density or high-curvature areas.

[0034] Step 105: performing a fusion process on the aligned real-scene 3D data and the aligned digital model according to the final fusion weight to generate a 3D terrain model, thereby achieving fusion modeling of the real-scene 3D data and the digital model.

[0035] In this step, the three-dimensional terrain model refers to the fused three-dimensional grid model containing surface details and global terrain features.

[0036] In an embodiment of the present invention, the elevation values of all three-dimensional points in the aligned real-scene three-dimensional data are weighted to generate a weighted average value. The elevation values of all center points in the aligned digital model are also weighted to generate a weighted reference value. The corresponding weighted average value and the weighted reference value are added to obtain a fused elevation value, and an elevation surface is constructed based on the value. The elevation surface is bound to the surface reflection properties of the aligned real-scene three-dimensional data to generate a three-dimensional terrain model.

[0037] The embodiments of the present invention achieve high-precision fusion modeling of real-scene three-dimensional data and digital models in complex terrain scenes, and improve the integrity of point cloud data in scenes where data is missing in shadow areas.

[0038] The present invention provides a specific embodiment, step 102, optimizing the real scene 3D data to generate optimized real scene 3D data, specifically comprising the following steps: Step 201: Based on the spatial distribution characteristics of the real-time point cloud data, phase compensation is performed on the three-dimensional points in the real-scene three-dimensional data that are disturbed by atmospheric scattering to obtain compensated three-dimensional points.

[0039] In this step, spatial distribution characteristics refer to the spatial arrangement characteristics of the 3D points in the real-world 3D data, such as density and spacing. Compensated 3D points are 3D points whose positional errors have been corrected through phase calibration, resulting in coordinates with higher accuracy than the original data.

[0040] In an embodiment of the present invention, the spatial density distribution of three-dimensional points in the real-scene three-dimensional data is first statistically analyzed, the ratio of the number of three-dimensional points in each preset sub-region to the region area is calculated, and low-density regions are identified; then, according to the distribution position of the low-density regions, the transmission power and pulse frequency of the lidar are dynamically adjusted so that the laser beam increases the number of scans in the low-density regions, and phase calibration is performed on the three-dimensional points disturbed by atmospheric scattering (that is, a compensation value is applied to the position coordinates of each three-dimensional point, and the compensation value is the deviation of the point from the theoretical position), to generate compensated three-dimensional points, whose position error is less than the preset tolerance threshold.

[0041] Step 202: Identify a target shadow area in the real-scene 3D data, and perform spatial interpolation reconstruction on the missing 3D points in the target shadow area according to the elevation change trend of the 3D points in the adjacent non-shadow area to generate virtual 3D points.

[0042] In this step, the target shadow area refers to the continuous, point-free area that the LiDAR cannot scan due to obstructions. The elevation trend refers to the increase or decrease in the elevation of 3D points in adjacent non-shadow areas along the terrain's direction. Missing 3D points refer to 3D points that should exist within the target shadow area but were not collected due to obstructions. Virtual 3D points are virtual locations generated by interpolation based on the elevation trends of adjacent areas and are used to fill in the shadow area.

[0043] In an embodiment of the present invention, all three-dimensional points are traversed and continuous target shadow areas without point cloud coverage are marked, whose boundaries are determined by the maximum spacing between adjacent valid three-dimensional points; the elevation values of three-dimensional points in adjacent non-shadow areas of the target shadow area are extracted, and the rate of change of the elevation values along the extension direction of the terrain (i.e., the elevation difference between adjacent points divided by the horizontal distance) is fitted. Virtual three-dimensional points are generated along the direction of the change rate at a preset step size, and their elevation values are linearly extrapolated based on the fitted change rate.

[0044] Step 203: merging the compensated 3D points and the virtual 3D points to generate optimized real-scene 3D data, wherein the point cloud coverage of the optimized real-scene 3D data reaches a preset coverage value.

[0045] In this step, point cloud coverage refers to the ratio of the area covered by the valid 3D point cloud to the total area of the preset terrain scene (i.e., the valid area divided by the total area). The valid area is defined as the minimum bounding rectangle containing at least one 3D point. The preset coverage value refers to a pre-set minimum point cloud coverage standard value, which is used to determine whether the optimized real-world 3D data meets modeling requirements. If the coverage does not reach this value, the data optimization process is iterated.

[0046] In an embodiment of the present invention, the coordinate set of the compensated three-dimensional points and the coordinate set of the virtual three-dimensional points are spatially superimposed, and points with overlapping coordinates are removed (i.e., only the compensated three-dimensional points are retained at the same position) to obtain a merged point cloud. The coverage rate of the merged point cloud in the preset terrain scene (the area of the effective point cloud region divided by the total area of the scene) is calculated. If the coverage rate does not reach the preset coverage value, phase compensation and interpolation reconstruction are re-executed until the threshold requirement is met, thereby generating optimized real-scene three-dimensional data.

[0047] The embodiment of the present invention achieves full coverage and high-precision optimization of real-scene three-dimensional data in complex terrain scenes by dynamically adjusting lidar parameters to compensate for atmospheric interference errors and reconstructing missing data in shadow areas based on the terrain continuity assumption.

[0048] The present invention provides a specific embodiment, step 202, identifying a target shadow area in the real-scene 3D data, and performing spatial interpolation reconstruction on the missing 3D points in the target shadow area based on the elevation change trend of the 3D points in the adjacent non-shadow area to generate virtual 3D points, specifically comprising the following steps: Step 211: Marking a closed area in the real-scene 3D data where the point cloud coverage is lower than a preset shadow threshold as an initial shadow area, and extracting a first boundary point of the initial shadow area.

[0049] In this step, the initial shadow area refers to a closed region with point cloud coverage below a preset shadow threshold, representing a surface blind spot that cannot be directly scanned by the lidar. The first boundary point is a 3D point at the edge of the initial shadow area, whose distance from the adjacent non-shadow area is less than a preset spacing, and is used to define the initial shadow range.

[0050] In an embodiment of the present invention, when marking a closed area in real-life three-dimensional data whose point cloud coverage is lower than a preset shadow threshold as an initial shadow area, all three-dimensional points in the real-life three-dimensional data are traversed, and the ratio of the number of point clouds in each preset sub-area to the area of the sub-area is counted. If the ratio is lower than the preset shadow threshold (such as 1%), the sub-area is marked as an initial shadow area; the three-dimensional points at the edge of the initial shadow area are detected to extract a first boundary point, which is a critical point whose horizontal distance from the adjacent three-dimensional point in the non-shadow area is less than a preset spacing (such as 0.2 meters).

[0051] Step 212: Expand the first boundary points according to the horizontal distribution range of the first boundary points and the preset buffer distance to generate a target shadow area.

[0052] In this step, the horizontal distribution range refers to the minimum bounding rectangle of the first boundary point on the horizontal plane (X / Y plane) and is used to determine the target shadow area expansion baseline. The preset buffer distance refers to a pre-set horizontal expansion distance (such as 0.5 meters) used to expand the initial shadow area repair range.

[0053] In an embodiment of the present invention, the minimum circumscribed rectangular range of the first boundary point is determined, and the rectangular range is extended in the horizontal direction by a preset buffer distance (such as 0.5 meters) to generate an extended target shadow area, which is the union of the initial shadow area and the extended buffer area, and the boundary point is defined as the second boundary point.

[0054] Step 213: Select at least three directional reference points from the second boundary points of the target shadow area, and extract three-dimensional points of adjacent non-shadow areas along the terrain extension direction of the directional reference points.

[0055] In this step, the second boundary points refer to the edge points of the target shadow area, generated by expanding the first boundary points and used to define the precise boundaries of the area to be repaired. The directional reference points are key points selected from the second boundary points and used to determine the terrain extension direction. The terrain extension direction is the dominant horizontal direction of the line connecting the directional reference points and the adjacent 3D points in the non-shadow area, which is used to constrain the interpolation path.

[0056] In an embodiment of the present invention, key points located in different terrain extension directions (such as the east, south, and west directions) are selected as direction reference points; when extracting three-dimensional points in adjacent non-shadow areas along the terrain extension direction of the direction reference point, the direction reference point is used as the starting point, and a preset search distance (such as 2 meters) is extended along the dominant horizontal direction (the average direction of the line connecting the reference point and the three-dimensional points in the adjacent non-shadow area) to extract all three-dimensional points in the extension direction.

[0057] Step 214: generating a plurality of terrain trend lines by fitting according to the elevation change trends of the three-dimensional points in the adjacent non-shadow areas.

[0058] In this step, the terrain trend line refers to a linear extension relationship generated based on the elevation change rate of adjacent three-dimensional points in the non-shadow area, and is used to infer the elevation value of the shadow area.

[0059] In an embodiment of the present invention, when fitting a terrain trend line based on the elevation change trend of the three-dimensional points in the adjacent non-shadow area, the operation is performed in the following manner: the three-dimensional points in the adjacent non-shadow area are sorted by horizontal distance, and the ratio of the elevation difference of the adjacent points to the horizontal distance (elevation difference / horizontal distance) is calculated; the average value of the ratio is used as the slope of the terrain trend line; and with the direction reference point as the starting point, a linearly extended terrain trend line is generated by fitting according to the slope.

[0060] Step 215: Using the slope of the terrain trend line as the first elevation change rate of the virtual three-dimensional point, the elevation value of the virtual three-dimensional point is calculated along the extension direction of the terrain according to a preset step size, and the first elevation change rate and the elevation value of the virtual three-dimensional point are combined to generate a virtual three-dimensional point.

[0061] In this step, the first elevation change rate refers to the ratio of elevation change over horizontal distance, calculated based on the slope of the terrain trend line. It describes the pattern of elevation changes along the terrain. The elevation value refers to the vertical height of a point on the surface, expressed in meters or other length units.

[0062] In an embodiment of the present invention, the horizontal distance of the current interpolation position is first calculated as the horizontal distance of the reference point + the preset step size × the current interpolation sequence number; then the elevation value of the current virtual 3D point is calculated as the elevation value of the direction reference point + the first elevation change rate × the horizontal distance of the current interpolation position; the elevation value of the current virtual 3D point is bound to the horizontal coordinate to generate a virtual 3D point. If the elevation difference between adjacent virtual 3D points exceeds a preset tolerance (e.g., 0.5 meters), the preset step size is reduced and the final virtual 3D point is recalculated.

[0063] The embodiments of the present invention solve the problem of accurately repairing missing data in shadow areas in complex terrain scenes. Specifically, the interpolation direction is constrained by terrain trend lines to avoid model distortion caused by sudden changes in elevation; the buffer distance and step size are dynamically adjusted to adapt to different terrain complexities; and the calculation path is simplified by direction reference points and trend lines to reduce the generation of redundant interpolation points.

[0064] The present invention provides a specific embodiment, step 104, determining the final fusion weight of the aligned real scene 3D data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set, specifically includes the following steps: Step 401: Divide the aligned dataset into a plurality of geographical units according to a preset size.

[0065] In this step, the preset size refers to the side length of the rectangular area divided by the geographic unit (e.g., 10 meters), which is used to control the granularity of data processing. A geographic unit is a rectangular area divided into fixed or dynamic sizes by the preset terrain scene and is the smallest spatial unit for data processing.

[0066] In an embodiment of the present invention, the aligned real-life three-dimensional data and the aligned digital model in the aligned dataset are divided into rectangular areas of fixed size according to the spatial scope of a preset terrain scene, and the side length of each rectangular area is dynamically set according to a terrain complexity parameter of the mountainous area. Flat areas use larger geographic units (e.g., 100 meters × 100 meters), and complex areas (e.g., mountains, building complexes) use smaller geographic units (e.g., 10 meters × 10 meters). The geographic unit is the smallest processing unit of the aligned dataset. Each geographic unit is an independent processing unit, and its boundaries are seamlessly connected with adjacent units to ensure the continuity of the terrain data.

[0067] Step 402: Count the number of point clouds of the aligned real-scene 3D data in each geographic unit, calculate the ratio of the number of point clouds to the area of the geographic unit, and use the ratio as the spatial distribution density value.

[0068] In this step, the spatial distribution density value refers to the ratio of the number of aligned real-life 3D point clouds within a geographic unit to the area of the geographic unit (number of point clouds / area), which is used to quantify the density of data details.

[0069] In an embodiment of the present invention, all 3D points of the aligned real-scene 3D data within the geographic unit are traversed, and the total number of 3D points is recorded; the number of point clouds is divided by the area of the geographic unit (side length multiplied by side length) to generate a spatial distribution density value, which represents the detail richness of the real-scene 3D data within the geographic unit.

[0070] Step 403: extracting the elevation values of all center points from the aligned digital models in the geographic unit to calculate a second elevation change rate between the center point and the center points of adjacent geographic units.

[0071] In this step, the second elevation change rate refers to the elevation difference of all center points in the aligned digital model in adjacent geographic units divided by the horizontal distance, which represents the steepness of the terrain slope.

[0072] In an embodiment of the present invention, the geometric center position of the aligned digital model in each geographic unit is first determined, and the elevation value of the aligned digital model at this position is read; the difference between the elevation value corresponding to the current center point and the elevation value corresponding to the center point of the adjacent unit is divided by the horizontal distance between the two units to generate a second elevation change rate.

[0073] Step 404: Calculate the terrain curvature value of the geographic unit according to the second elevation change rate.

[0074] In this step, the terrain curvature value refers to an indicator of terrain curvature calculated based on the average difference in elevation change rate, and is used to identify complex terrains such as cliffs and gullies.

[0075] In an embodiment of the present invention, the second elevation change rate of the current geographic unit and all its adjacent units is extracted, and the average difference between the second elevation change rate of the current geographic unit and the second elevation change rate of the adjacent units is calculated as the terrain curvature value.

[0076] Step 405: Determine the fusion weight to be verified between the aligned real-scene 3D data and the aligned digital model according to the spatial distribution density value and the terrain curvature value.

[0077] In this step, the fusion weight to be verified refers to the fusion ratio coefficient of the initially assigned aligned real-scene 3D data and the aligned digital model, and its validity needs to be confirmed through the verification step.

[0078] In an embodiment of the present invention, when determining the fusion weight to be verified based on the spatial distribution density value and the terrain curvature value, the following operations are performed: if the spatial distribution density value is greater than a preset density threshold (such as 50 points per square meter), the weight of the aligned real-scene three-dimensional data is increased; if the terrain curvature value is greater than a preset curvature threshold (such as 0.1 radians / meter), the weight of the aligned digital model is reduced; for areas that meet the above conditions at the same time, the spatial distribution density value and the terrain curvature value are added in a preset ratio (such as the spatial distribution density value multiplied by 0.6 plus the terrain curvature value multiplied by 0.4) to generate the fusion weight to be verified.

[0079] Step 406: Verify whether the fusion weight to be verified is within a preset range. If not, repeat the step of dividing the aligned dataset into multiple geographic units according to a preset size until a predetermined convergence state is reached, complete the iterative update, and obtain the final fusion weight. The predetermined convergence state is that the final fusion weight is within a preset range.

[0080] In this step, the preset interval refers to the reasonable value range of the final fusion weight (such as 0.3~0.7), which is used to determine whether the weight is acceptable.

[0081] In an embodiment of the present invention, when verifying whether the fusion weight to be verified is within a preset range (e.g., 0.3-0.7), the fusion weights to be verified of all geographic units are traversed, and the geographic units that exceed the range are marked; if there are out-of-limit units, the preset size is adjusted (e.g., the size of the geographic unit is reduced to 5 meters × 5 meters) and the geographic units are re-divided, and steps 401 to 405 are repeated until all the fusion weights to be verified are within the preset range, the iterative update is completed, and the final fusion weight is output.

[0082] The embodiments of the present invention solve the problem of dynamic adaptation of weights in the fusion of real-life 3D data and digital models in complex terrain scenes. Specifically, dual threshold constraints of density and curvature ensure the rationality of weights in high-detail areas; iterative adjustment of geographic unit size suppresses weight anomalies caused by sudden terrain changes.

[0083] The present invention provides a specific embodiment, step 103, performing spatiotemporal alignment on the optimized real-scene 3D data and the digital model to generate an aligned dataset, wherein the aligned dataset includes the aligned real-scene 3D data and the aligned digital model, specifically comprising the following steps: Step 301: extracting the three-dimensional coordinates of the geographic control points in the optimized real-scene three-dimensional data and the elevation reference values of the corresponding geographic locations in the digital model.

[0084] In this step, geographic control points refer to 3D points with significant curvature and reflectivity in the optimized real-world 3D data, serving as reference points for coordinate system transformation. Elevation reference values refer to the standardized surface height values corresponding to geographic locations in the digital model.

[0085] In an embodiment of the present invention, first, three-dimensional points with significant terrain features are screened from the optimized real-scene three-dimensional data as geographic control points, wherein the three-dimensional points with significant terrain features are three-dimensional points whose curvature values are greater than a preset threshold and whose reflectivity differences exceed a set range; then, the plane coordinates of the geographic control points are projected into the coordinate system of the digital model to obtain the three-dimensional coordinates and elevation reference values of the geographic control points at the projected positions, wherein the three-dimensional coordinates of the geographic control points include horizontal positions (X / Y) and vertical heights (Z).

[0086] Step 302: Calculate the spatial position conversion parameters of the optimized real-scene 3D data relative to the digital model based on the difference between the 3D coordinates of the geographic control point and the elevation reference value. The spatial position conversion parameters include translation parameters, rotation parameters, and scaling parameters.

[0087] In this step, the spatial position conversion parameters refer to a set of three types of parameters: translation, rotation, and scaling. They are used to dynamically map the local coordinate system of the optimized real-world 3D data to the global coordinate system of the digital model. The translation parameter refers to the average value of the elevation datum difference between the optimized real-world 3D data and the digital model, and is used to eliminate the vertical datum deviation between the real-world 3D data and the digital model. The rotation parameter refers to the horizontal correction calculated based on the azimuth deviation between the horizontal coordinates (X / Y) of the geographic control point and the plane coordinates of the digital model, and is used to correct the horizontal deviation between the optimized real-world 3D data and the digital model. The scaling parameter refers to the proportional coefficient between the optimized real-world 3D data and the digital model at the same geographic span, and is used to unify the spatial scale datum of the optimized real-world 3D data and the digital model.

[0088] In an embodiment of the present invention, the spatial position conversion parameters are calculated based on the difference between the three-dimensional coordinates of the geographic control points and the elevation reference value, specifically including: calculating the average difference between the Z coordinates of all geographic control points and the corresponding elevation reference value (that is, the sum of all differences divided by the number of geographic control points) to obtain the translation parameter; calculating the rotation parameter based on the deviation angle between the horizontal coordinates of the geographic control points and the plane coordinates of the digital model; and determining the scaling factor based on the distance ratio between the optimized real-scene three-dimensional data and the digital model at the same geographic span (the distance between two points in the real-scene data divided by the distance between corresponding points in the digital elevation model).

[0089] Step 303: Based on the spatial position conversion parameters, coordinate conversion is performed on all three-dimensional points of the optimized real-scene three-dimensional data to generate converted real-scene three-dimensional data.

[0090] In this step, the converted real-scene three-dimensional data refers to the three-dimensional point cloud data that is consistent with the digital model space reference after the coordinate system transformation.

[0091] In an embodiment of the present invention, when the coordinate transformation of the optimized real-scene three-dimensional data is performed based on the spatial position transformation parameters, the following operations are performed on the original coordinates of each three-dimensional point: the Z coordinate is subtracted by the translation parameter; the horizontal coordinate direction is adjusted according to the rotation parameter; the transformed coordinate is multiplied by the scaling parameter to generate the transformed real-scene three-dimensional data, whose coordinate system is consistent with the global coordinate system of the digital model, and the elevation reference is aligned with the digital model.

[0092] Step 304: performing time synchronization processing on the converted real-scene 3D data and the digital model to obtain synchronized data.

[0093] In this step, the synchronized data refers to the converted real scene 3D data and digital model data segments with matching time windows and spatial alignment.

[0094] In an embodiment of the present invention, a time window is divided according to the timestamp of the digital model, and the collected data overlapping with the time window is extracted from the converted real-scene three-dimensional data; at the same time, the data segments in the digital model whose timestamps fall into the same time window are filtered to generate synchronized data.

[0095] Step 305: Merge the converted real-scene 3D data and the synchronized data into an aligned data set.

[0096] In an embodiment of the present invention, when the converted real-life 3D data and the synchronized data are merged into an aligned dataset, the two types of data are superimposed according to spatial grid units: if both synchronized real-life 3D data and digital model data exist in a certain grid unit, all data points are retained; if only a single data source exists in a certain grid unit, it is marked as an area to be completed, and ultimately an aligned dataset containing spatially aligned, temporally synchronized, and data-complete real-life 3D data and terrain reference data is generated.

[0097] The embodiment of the present invention solves the problem of inconsistent spatiotemporal references between real-life 3D data and digital models in complex terrain scenarios through a multi-parameter coordinate system conversion and time window matching mechanism driven by geographic control points, thereby improving adaptability to dynamic terrain.

[0098] The present invention provides a specific embodiment, step 302, calculating the spatial position conversion parameters of the optimized real scene 3D data relative to the digital model based on the difference between the 3D coordinates of the geographic control point and the elevation reference value, wherein the spatial position conversion parameters include translation parameters, rotation parameters, and scaling parameters, specifically comprising the following steps: Step 311: Calculate the difference between the vertical coordinate of the geographic control point and the corresponding elevation reference value to generate a translation parameter to be verified based on the difference.

[0099] In an embodiment of the present invention, all geographic control points are traversed, and the vertical coordinate (Z value) of each geographic control point is subtracted from the elevation reference value of the corresponding geographic location in the digital model to obtain a vertical deviation value of each control point; the vertical deviation values are summed and divided by the number of geographic control points to generate a translation parameter to be verified, which represents the overall elevation offset of the optimized real-life three-dimensional data relative to the digital model.

[0100] Step 312: Calculate the azimuth deviation between the horizontal coordinates of the geographic control point and the plane coordinates corresponding to the digital model to generate an average rotation angle, convert the average rotation angle into a horizontal direction correction value, and use the horizontal direction correction value as the rotation parameter to be verified; In this step, the azimuth deviation value refers to the azimuth difference (in degrees) between the horizontal coordinate of the geographic control point and the corresponding horizontal coordinate of the digital elevation model. The average rotation angle is the arithmetic mean of the azimuth deviation values of all geographic control points. The horizontal correction value is the parameter that converts the average rotation angle into a coordinate system rotation (for example, the change in coordinate axis per degree of rotation).

[0101] In an embodiment of the present invention, for each geographic control point, the azimuth difference between its horizontal coordinate (X / Y) and the corresponding plane coordinate of the digital model is calculated (i.e., the angle difference between the line connecting the two points and the reference direction); the azimuth deviation values of all geographic control points are summed and divided by the number of geographic control points to generate an average rotation angle; the average rotation angle is converted into a horizontal correction value (e.g., the angle difference is converted into a coordinate axis rotation value) as a rotation parameter to be verified, so as to correct the horizontal deviation between the real three-dimensional data and the digital model.

[0102] Step 313: Select at least two pairs of geographic control points, calculate the actual distance between each pair of geographic control points in the optimized real-life 3D data and the theoretical distance between the corresponding geographic control points in the digital model, and generate a scaling parameter to be verified based on the actual distance and the theoretical distance.

[0103] In this step, the actual distance refers to the 3D Euclidean distance between the geographic control point pairs in the optimized real-world 3D data. The theoretical distance refers to the 2D planar distance between the corresponding geographic control point pairs in the digital model.

[0104] In an embodiment of the present invention, when selecting at least two pairs of geographic control points, pairs of geographic control points with symmetrical spatial distribution and significant distance differences are selected; the actual distance of each pair of geographic control points in the optimized real-life three-dimensional data (the three-dimensional Euclidean distance between the two points) is calculated, and the theoretical distance of the corresponding geographic control points in the digital model (the two-dimensional Euclidean distance in a plane coordinate system) is calculated; the actual distance of each pair of points is divided by the theoretical distance to obtain multiple scale coefficients; the median of all scale coefficients is taken to generate a scaling parameter to be verified, which represents the spatial scale ratio relationship between the real-life three-dimensional data and the digital model.

[0105] Step 314: Verify the translation parameter to be verified, the rotation parameter to be verified, and the scaling parameter to be verified to generate spatial position conversion parameters that meet a preset tolerance condition.

[0106] In this step, the preset tolerance conditions refer to the validity judgment criteria of the spatial position conversion parameters (such as the scaling parameter range, the matching degree of the rotation angle and the terrain direction).

[0107] In this embodiment of the present invention, when verifying the translation, rotation, and scaling parameters, the scaling parameter is checked to see if it is within a preset reasonable range (e.g., 0.8 to 1.2). If it is outside this range, the parameter is deemed invalid. The logical consistency of the translation and rotation parameters is also checked (e.g., whether the rotation angle matches the terrain direction). If all parameters meet the preset tolerance conditions, the final spatial position transformation parameters are output. Otherwise, geographic control points are reselected and steps 311 to 313 are iterated until the parameters pass verification.

[0108] The embodiments of the present invention solve the accuracy and stability issues of spatial benchmark conversion of multi-source data in complex terrain scenarios; suppress the interference of outliers by filtering scaling parameters through the median; trigger the reselection of control points by parameter verification to adapt to local sudden changes in terrain; and preset tolerance conditions ensure that the output parameters meet modeling requirements.

[0109] The present invention provides a specific embodiment, step 105, fusing the aligned real scene 3D data and the aligned digital model according to the final fusion weight to generate a 3D terrain model, specifically comprising the following steps: Step 501: extracting the elevation values of all 3D points from the aligned real-scene 3D data in the geographic unit, and calculating the real-scene elevation contribution value by combining the final fusion weight.

[0110] In this step, the real-scene elevation contribution value refers to the result of multiplying the arithmetic mean of the elevation values of the 3D points of the aligned real-scene 3D data within the geographic unit by the final fusion weight, which represents the degree of influence of the aligned real-scene 3D data within the geographic unit on the details of the final terrain.

[0111] In an embodiment of the present invention, the elevation values of all three-dimensional points in the aligned real-scene three-dimensional data of the geographic unit are traversed, and the final fusion weight of the corresponding geographic unit is multiplied by the arithmetic mean of the elevation value (that is, the sum of the elevation values of all three-dimensional points is divided by the number of three-dimensional points) to generate a weighted real-scene elevation contribution value.

[0112] Step 502: Calculate a base elevation contribution value based on the elevation values of all center points in the aligned digital model in the geographic unit and the final fusion weight.

[0113] In this step, the benchmark elevation contribution value refers to the result of multiplying the elevation value of the digital model at the center point of the geographic unit by the benchmark weight (1-final fusion weight), which represents the global benchmark support role of the aligned digital model data within the geographic unit on the final terrain.

[0114] In an embodiment of the present invention, based on the elevation values of all center points in the aligned digital model in the geographic unit read in step 403, the benchmark weight corresponding to the geographic unit (i.e., 1 minus the final fusion weight) is multiplied by the elevation value of the center point to generate a weighted benchmark elevation contribution value.

[0115] Step 503: combining the actual elevation contribution value and the reference elevation contribution value to calculate the fused elevation value corresponding to each geographic unit, so as to construct an elevation surface covering the preset terrain scene.

[0116] In this step, the fused elevation value refers to the sum of the weighted average and the weighted reference value, representing the final elevation value adopted by the geographic unit. The elevation surface refers to the continuous terrain surface geometric model generated by the fused elevation values of all geographic units.

[0117] In an embodiment of the present invention, the actual scene elevation contribution value and the reference elevation contribution value are directly added to obtain a fused elevation value, and then the fused elevation value of each geographic unit is obtained; when constructing an elevation surface covering the preset terrain scene, the fused elevation values of all geographic units are arranged into a two-dimensional matrix according to their spatial positions, and the elevation values of the uncovered areas in the matrix are filled in by an interpolation algorithm to form a continuous elevation surface, which is a continuous terrain surface geometric expression generated by discrete fused elevation values.

[0118] Step 504: Bind the elevation surface with the surface reflection properties of the aligned real-scene 3D data to generate a 3D terrain model.

[0119] In this step, the surface reflection attribute refers to the optical characteristic data such as visible light reflectance and color recorded in the aligned real-scene 3D data.

[0120] In an embodiment of the present invention, the reflectivity and color information recorded in the aligned real-scene three-dimensional data are mapped to the corresponding position of the elevation surface according to the spatial coordinates of the three-dimensional points; the geometric data of the elevation surface and the mapped surface reflection properties are combined and encapsulated into a three-dimensional model containing geometric shapes and surface features. The three-dimensional terrain model is a fusion product that has both elevation accuracy and visual authenticity.

[0121] The embodiments of the present invention avoid the high complexity of global calculations and improve processing efficiency; by combining the dynamic balancing strategy of the real-scene elevation contribution value and the benchmark elevation contribution value, it realizes the efficient fusion of multi-source data in complex terrain scenes, and retains the local details of the aligned real-scene data and the global consistency of the aligned digital model.

[0122] Figure 2 The present invention provides a structural diagram of a modeling system for integrating real-scene 3D data with digital models. Figure 2 As shown, the system includes: An acquisition module 21 is configured to acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model; An optimization module 22 is configured to optimize the real-scene 3D data to generate optimized real-scene 3D data; an alignment module 23 for performing spatiotemporal alignment between the optimized real-scene 3D data and the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene 3D data and the aligned digital model; A determination module 24 is configured to determine a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; The generating module 25 is configured to fuse the aligned real-scene 3D data with the aligned digital model according to the final fusion weight to generate a 3D terrain model, thereby achieving modeling by fusing the real-scene 3D data with the digital model.

[0123] Figure 2 The modeling system that integrates real-scene 3D data with digital models can be executed Figure 1 The implementation principles and technical effects of the modeling method for fusing real-world 3D data with a digital model described in the illustrated embodiment will not be elaborated upon. The specific manner in which the various modules and units perform operations in the modeling system for fusing real-world 3D data with a digital model in the aforementioned embodiment have been described in detail in the relevant embodiments of the method and will not be elaborated upon here.

[0124] In one possible design, Figure 2 The modeling system for fusing real-scene 3D data with digital models in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0125] The processing component 32 is used to: obtain real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain benchmark, wherein the digital model includes: a digital elevation model and / or a digital surface model; optimize the real-scene three-dimensional data to generate optimized real-scene three-dimensional data; perform spatiotemporal alignment on the optimized real-scene three-dimensional data and the digital model to generate an aligned data set, wherein the aligned data set includes aligned real-scene three-dimensional data and an aligned digital model; determine the final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; and perform fusion processing on the aligned real-scene three-dimensional data and the aligned digital model according to the final fusion weight to generate a three-dimensional terrain model, so as to realize modeling of the fusion of real-scene three-dimensional data and digital model.

[0126] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0127] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0128] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0129] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0130] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0131] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0132] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The embodiment shown is a modeling method for fusing real-scene three-dimensional data with a digital model.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0135] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A modeling method for integrating real-scene three-dimensional data with digital models, characterized in that: include: Acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model; Optimizing the real-scene three-dimensional data to generate optimized real-scene three-dimensional data; Performing spatiotemporal alignment on the optimized real-scene three-dimensional data and the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene three-dimensional data and the aligned digital model; Determining a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; According to the final fusion weight, the aligned real scene 3D data and the aligned digital model are fused to generate a 3D terrain model, so as to achieve modeling by fusion of the real scene 3D data and the digital model.

2. The method according to claim 1, characterized in that Optimizing the real-scene three-dimensional data to generate optimized real-scene three-dimensional data includes: Based on the spatial distribution characteristics of the real-time point cloud data, phase compensation is performed on the three-dimensional points in the real-scene three-dimensional data that are disturbed by atmospheric scattering to obtain compensated three-dimensional points; Identifying a target shadow area in the real-scene 3D data, and reconstructing missing 3D points in the target shadow area by spatial interpolation based on elevation change trends of 3D points in adjacent non-shadow areas to generate virtual 3D points; The compensated three-dimensional points and the virtual three-dimensional points are merged to generate optimized real-scene three-dimensional data, and the point cloud coverage of the optimized real-scene three-dimensional data reaches a preset coverage value.

3. The method according to claim 2, characterized in that Identifying a target shadow area in the real-scene 3D data, and performing spatial interpolation reconstruction on missing 3D points in the target shadow area based on elevation change trends of 3D points in adjacent non-shadow areas to generate virtual 3D points, including: Marking a closed area in the real-scene three-dimensional data where the point cloud coverage is lower than a preset shadow threshold as an initial shadow area, and extracting a first boundary point of the initial shadow area; Expanding the first boundary points according to the horizontal distribution range of the first boundary points and a preset buffer distance to generate a target shadow area; Selecting at least three directional reference points from the second boundary points of the target shadow area, and extracting three-dimensional points of adjacent non-shadow areas along the terrain extension direction of the directional reference points; Fitting and generating a plurality of terrain trend lines according to the elevation change trends of the three-dimensional points in the adjacent non-shadow areas; The slope of the terrain trend line is used as the first elevation change rate of the virtual three-dimensional point, and the elevation value of the virtual three-dimensional point is calculated along the extension direction of the terrain according to a preset step size. The virtual three-dimensional point is generated by combining the first elevation change rate and the elevation value of the virtual three-dimensional point.

4. The method according to claim 1, wherein Determining a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and the terrain curvature value of the aligned data set includes: Dividing the aligned data set into a plurality of geographic units according to a preset size; Counting the number of point clouds of the aligned real-scene three-dimensional data within each geographic unit, calculating a ratio of the number of point clouds to the area of the geographic unit, and using the ratio as a spatial distribution density value; Extracting elevation values of all center points from the aligned digital models in the geographic unit to calculate a second elevation change rate between the center point and the center points of adjacent geographic units; Calculating a terrain curvature value of the geographic unit according to the second elevation change rate; Determining a fusion weight to be verified between the aligned real-scene three-dimensional data and the aligned digital model according to the spatial distribution density value and the terrain curvature value; Verify whether the fusion weight to be verified is within a preset range. If not, repeatedly divide the aligned dataset into multiple geographic units according to a preset size until a predetermined convergence state is reached, complete the iterative update, and obtain the final fusion weight. The predetermined convergence state is that the final fusion weight is within the preset range.

5. The method according to claim 1, wherein Performing spatiotemporal alignment on the optimized real-scene three-dimensional data and the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene three-dimensional data and the aligned digital model, including: Extracting the three-dimensional coordinates of the geographic control points in the optimized real-scene three-dimensional data and the elevation reference values of the corresponding geographic locations in the digital model; Calculating spatial position conversion parameters of the optimized real-scene 3D data relative to the digital model based on the difference between the 3D coordinates of the geographic control point and the elevation reference value, wherein the spatial position conversion parameters include translation parameters, rotation parameters, and scaling parameters; Based on the spatial position conversion parameters, coordinate conversion is performed on all three-dimensional points of the optimized real-scene three-dimensional data to generate converted real-scene three-dimensional data; Performing time synchronization processing on the converted real-scene three-dimensional data and the digital model to obtain synchronized data; The converted real-scene three-dimensional data and the synchronized data are merged into an aligned data set.

6. The method according to claim 5, characterized in that According to the difference between the three-dimensional coordinates of the geographic control point and the elevation reference value, the spatial position conversion parameters of the optimized real scene three-dimensional data relative to the digital model are calculated, and the spatial position conversion parameters include translation parameters, rotation parameters and scaling parameters, including: Calculating the difference between the vertical coordinate of the geographic control point and the corresponding elevation reference value to generate a translation parameter to be verified according to the difference; Calculating the azimuth deviation between the horizontal coordinates of the geographic control point and the plane coordinates corresponding to the digital model to generate an average rotation angle, converting the average rotation angle into a horizontal direction correction value, and using the horizontal direction correction value as a rotation parameter to be verified; selecting at least two pairs of geographic control points, calculating an actual distance between each pair of geographic control points in the optimized real-life three-dimensional data and a theoretical distance between corresponding geographic control points in the digital model, and generating a scaling parameter to be verified based on the actual distance and the theoretical distance; The translation parameter to be verified, the rotation parameter to be verified, and the scaling parameter to be verified are verified to generate spatial position conversion parameters that meet a preset tolerance condition.

7. The method according to claim 1, characterized in that According to the final fusion weight, the aligned real scene 3D data and the aligned digital model are fused to generate a 3D terrain model, including: Extracting the elevation values of all 3D points from the aligned real-scene 3D data in the geographic unit, and calculating the real-scene elevation contribution value by combining the final fusion weights; Calculating a base elevation contribution value based on the elevation values of all center points in the aligned digital model in the geographic unit and the final fusion weight; Combining the actual elevation contribution value and the reference elevation contribution value, calculating the fused elevation value corresponding to each geographic unit to construct an elevation surface covering the preset terrain scene; The elevation surface is bound to the surface reflection properties of the aligned real-scene three-dimensional data to generate a three-dimensional terrain model.

8. A modeling system that integrates real-scene 3D data with digital models, characterized in that: include: An acquisition module is used to acquire real-time point cloud data in a preset terrain scene, use the real-time point cloud data as real-scene three-dimensional data, and load a digital model as a global terrain reference, wherein the digital model includes: a digital elevation model and / or a digital surface model; An optimization module, configured to optimize the real-scene three-dimensional data to generate optimized real-scene three-dimensional data; an alignment module, configured to perform spatiotemporal alignment of the optimized real-scene 3D data with the digital model to generate an aligned data set, wherein the aligned data set includes the aligned real-scene 3D data and the aligned digital model; a determination module, configured to determine a final fusion weight of the aligned real-scene three-dimensional data and the aligned digital model based on the spatial distribution density and terrain curvature value of the aligned data set; A generation module is used to fuse the aligned real-scene 3D data with the aligned digital model according to the final fusion weight to generate a 3D terrain model, so as to achieve modeling of the fusion of the real-scene 3D data and the digital model.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a modeling method for fusing real-life three-dimensional data with a digital model as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a modeling method for fusing real-scene three-dimensional data with a digital model as described in any one of claims 1 to 7 is implemented.

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