CN112927360A - Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data - Google Patents
Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data Download PDFInfo
- Publication number
- CN112927360A CN112927360A CN202110311937.0A CN202110311937A CN112927360A CN 112927360 A CN112927360 A CN 112927360A CN 202110311937 A CN202110311937 A CN 202110311937A CN 112927360 A CN112927360 A CN 112927360A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- model
- dimensional
- dimensional modeling
- oblique
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000004927 fusion Effects 0.000 title claims abstract description 33
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000013507 mapping Methods 0.000 claims abstract description 10
- 238000012937 correction Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 238000009499 grossing Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 10
- 230000009466 transformation Effects 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000005070 sampling Methods 0.000 description 8
- 238000000605 extraction Methods 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000013519 translation Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 238000009616 inductively coupled plasma Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 241000238097 Callinectes sapidus Species 0.000 description 1
- 241001122767 Theaceae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000002310 reflectometry Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a three-dimensional modeling method and a three-dimensional modeling system based on fusion of an oblique model and laser point cloud data, wherein a data processing module is used for reading, storing and processing the oblique photogrammetry module and a three-dimensional laser scanning module through an algorithm to obtain a basic data source for three-dimensional modeling of a task target area and outputting a high-precision three-dimensional model of the task target area; the method comprises the steps of unifying point clouds matched with an unmanned aerial vehicle oblique image and point clouds matched with a close-range image to the same space reference standard to achieve effective fusion of point cloud data, repairing missing image point clouds of oblique photography modeling through ground laser point cloud, constructing a triangular net on the point cloud basis, meanwhile, smoothing and simplifying the triangular net, and finally automatically mapping textures based on the oblique photography image to generate a high-precision three-dimensional model.
Description
Technical Field
The invention relates to the technical field of urban three-dimensional modeling, in particular to a three-dimensional modeling method and a three-dimensional modeling system based on fusion of an inclined model and laser point cloud data.
Background
Oblique photography is an efficient and low-cost three-dimensional modeling technology, and is more and more widely applied to urban three-dimensional reconstruction, but because ground image data is acquired in an aerial operation mode, the local shielding phenomenon is inevitably generated due to the influences of factors such as terrain, buildings, wind direction and the like, point cloud loopholes are generated in the air-three encryption processing process, and the three-dimensional model generates the phenomena of cavities, flower drawing, deformation and the like at corresponding parts.
The three-dimensional laser scanning technology is characterized in that a three-dimensional model of a measured object and various drawing data such as lines, surfaces and bodies can be quickly reconstructed by recording information such as three-dimensional coordinates, reflectivity and texture of a large number of dense points on the surface of the measured object by utilizing a laser ranging principle. The three-dimensional laser scanning technology still has some defects in application, such as scanning blind areas at the tops of high-rise buildings and structures, unsatisfactory camera shooting visual angle, low post-processing software processing efficiency and the like.
Aiming at the respective advantages and disadvantages of oblique photogrammetry and three-dimensional laser scanning technologies, aiming at solving the shielding problem of the oblique photogrammetry technology and realizing the refinement of the model, the point cloud data of the building facade acquired by an airborne laser radar mobile measurement system is fused on the basis of oblique photogrammetry data to jointly construct the three-dimensional model of the urban building, and the problems of the pattern drawing, the cavity and the deformation of the model are effectively solved.
Due to the limitation of the oblique photography shooting angle and the operation method, the three-dimensional point cloud encrypted by the space-triplet mode has a cavity in a shielded area, and a complete three-dimensional model cannot be established, so that the attractiveness and integrity of the model are defective. The laser point cloud has the advantages of high precision and high density, so at present, researches for fusing the laser point cloud and the laser point cloud are carried out, for example, the Dennon doctor in Wuhan province tries to acquire the three-dimensional point cloud by establishing a stereopair and then carries out registration with point cloud data acquired by laser radar equipment.
Most software at present adopts a point cloud fusion technology based on an ICP (inductively coupled plasma) algorithm, and the technology is an Iterative Closest Point (ICP) algorithm based on a local feature matching initial value. The point cloud registration is a process of calculating the accurate mapping of the space geometry relationship between point cloud sets obtained in different modes, solving coordinate conversion parameters and carrying out rigid body transformation on a data set to be converted. The ICP algorithm is an iterative algorithm of global registration, and very high registration accuracy can be obtained. Due to the limitations of the current technology, identifying and matching geometric features in fusion work still faces 3 bottlenecks:
1. geometric natural features of sparse or non-artificially constructed areas of buildings around cities are difficult to extract, and ground can be used as feature elements for registration;
2. the redundancy of the geometric features extracted based on the feature vectors is strong, and due to the influence of precision and scale difference, similarity measurement results of one-to-many, many-to-many and null matching are generally caused and need to be solved by utilizing methods such as probability relaxation constraint matching feature lines and the like;
3. the change information of the multi-temporal point cloud can interfere with the identification and extraction of the same geometric features.
In various current novel surveying and mapping means, the three-dimensional modeling of unmanned aerial vehicle oblique photography is a high-efficiency, high-precision and low-cost three-dimensional modeling technology, and is increasingly widely applied to the aspects of urban three-dimensional reconstruction, real-scene China and the like. However, as the ground image data is acquired by the aerial operation, a local shielding phenomenon is inevitably generated, so that a point cloud leak occurs in the air-to-three encryption processing process, and the three-dimensional model has the phenomena of void, flower drawing, deformation and the like at the corresponding position. In order to solve the shielding problem of the oblique photography three-dimensional modeling technology, improve the modeling precision of the model and provide a method for fusion modeling of oblique photography and ground laser point cloud, an ICP algorithm is inevitably required.
The ICP algorithm involves image registration, which is a typical problem and technical difficulty in the field of image processing research, and aims to compare or fuse images acquired under different conditions for the same object, for example, the images may be from different acquisition devices, may be captured by a SONY camera, may be captured by a nikon camera, and may be taken from different times, different lighting conditions, different capturing perspectives, and the like, and sometimes the image registration problem for different objects is also needed. Specifically, for two images in a set of image data sets, one image is mapped to the other image by finding a spatial transformation, so that points corresponding to the same position in space in the two images are in one-to-one correspondence, and the purpose of information fusion is achieved. A classical application is scene reconstruction, for example, a tea table is provided with a plurality of cups, a depth camera is used for scanning a scene, it is generally impossible to complete scanning of objects in the scene by one acquisition, only point clouds from different angles of the scene are obtained, and then the point clouds are fused together to obtain a complete scene.
Disclosure of Invention
In order to solve the above problems, an aspect of the present invention provides a three-dimensional modeling method based on fusion of a tilt model and laser point cloud data, and the technical scheme of the present invention is as follows:
a three-dimensional modeling method based on fusion of an inclined model and laser point cloud data comprises the following steps:
acquiring a basic data source of three-dimensional modeling of a task target area through an oblique photogrammetry module and a three-dimensional laser scanning module, wherein the basic data source of three-dimensional modeling comprises an oblique image acquired by the oblique photogrammetry module and a close-range image acquired by the three-dimensional laser scanning module;
the data processing module establishes a model based on the oblique image to obtain an oblique model point cloud of a task target area; establishing a model based on the close-range image to obtain a laser point cloud of a task target area;
the data processing module acquires the tilt model point cloud and the laser point cloud, and performs coarse registration on the two data by using a registration tool so that the two data are corrected in the same system and overlapped to obtain a coarse registration model of a task target area;
after the data processing module obtains the rough registration model, a computer algorithm tool is used for carrying out precise registration correction on the point cloud so as to enable the point cloud to be closely attached together, and effective fusion of the point cloud of the tilt model and the point cloud data of the laser is completed;
the data processing module constructs a triangulation network on the basis of the effectively fused point cloud, meanwhile, the triangulation network is smoothed and simplified, and a high-precision three-dimensional model of a task target area is generated by automatically mapping textures on the basis of oblique photography images.
As a further illustration of the invention, the oblique photogrammetry module acquires the oblique images based on unmanned aerial vehicle oblique flight photography.
Furthermore, the data processing module comprises Context Capture software, and the tilt model point cloud and the laser point cloud are fused in the Context Capture.
Further, the rough registration is carried out in a characteristic point mode, a plurality of obvious characteristic points of the tilt model point cloud and the laser point cloud are selected, and the registration toolbar of the Context Capture is utilized to carry out the rough registration of the two data.
Furthermore, a certain degree of overlap is kept between the oblique image and the close-up image so as to extract feature points.
Furthermore, the data processing module comprises DP-Modeller software, when the tilt model point cloud and the laser point cloud are not fused to cause the established three-dimensional model to be disconnected, the DP-Modeller software is used for seamlessly integrating the models established by the tilt image and the close-range image, the overlapping position of the two models is leveled in the DP-Modeller software, and the leveled tile and the fine tile in the close-range model are placed in a folder for calling.
Further, an algorithm for performing fine registration correction on the point cloud is an ICP algorithm.
Furthermore, in the mapping texture, an image with the optimal resolution is selected from the multiple source images for texture mapping, so as to obtain a high-precision real three-dimensional model.
In another aspect of the present invention, a three-dimensional modeling system based on fusion of a tilt model and laser point cloud data is provided, which includes:
the oblique photography measurement module is used for acquiring an oblique image of the three-dimensional modeling of the task target area;
the three-dimensional laser scanning module is used for acquiring a close-range image of a task target area three-dimensional modeling;
and the data processing module is used for reading, storing and processing the oblique photogrammetry module and the three-dimensional laser scanning module by an algorithm to obtain a basic data source for three-dimensional modeling of the task target area and output a high-precision three-dimensional model of the task target area.
Still further, the data processing module comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the three-dimensional modeling method based on fusion of the tilt model and the laser point cloud data according to any one of claims 1 to 8 when executing the computer program.
The invention has the beneficial effects that:
according to the method, the point cloud matched with the oblique image of the unmanned aerial vehicle and the point cloud matched with the close-range image are unified to the same space reference datum to realize effective fusion of the point cloud data, the image point cloud missing in oblique photography modeling is repaired through ground laser point cloud, a triangular net is constructed on the point cloud basis, meanwhile, the triangular net is smoothed and simplified, and finally, the texture is automatically mapped based on the oblique photography image, so that a high-precision three-dimensional model can be generated.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a first schematic diagram of point cloud matching according to the present invention;
FIG. 3 is a second schematic diagram of point cloud matching according to the present invention;
FIG. 4 is a diagram of an embodiment of a tilt model point cloud;
FIG. 5 is a diagram illustrating an embodiment of a laser point cloud;
FIG. 6 is an exemplary diagram of coarse registration effect of tilt model point cloud and laser point cloud according to the present invention;
FIG. 7 is an exemplary diagram of the fine registration correction effect of the tilt model point cloud and laser point cloud ICP algorithm of the invention;
FIG. 8 is a comparison graph before and after the present invention is fine registered by the ICP algorithm;
FIG. 9 is a schematic diagram illustrating the calculation principle of the overlapping degree between the oblique image and the close-up image according to the present invention.
Detailed Description
Example (b):
the embodiments of the present invention will be described in detail with reference to the accompanying drawings, and it is to be understood that the described embodiments are merely a part of the embodiments of the invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", "first", "second", etc. indicate orientations or positional or sequential relationships based on those shown in the drawings, and are only for convenience in describing and simplifying the present invention, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
Referring to a flow chart shown in the attached figure 1, the invention discloses a three-dimensional modeling method based on the fusion of an inclined model and laser point cloud data, which takes data obtained by an unmanned aerial vehicle aerial photogrammetry and ground close-range photogrammetry system as a basic data source for real-range three-dimensional modeling, combines a camera file and an image exterior orientation element provided by an unmanned aerial vehicle POS (position) system, and uses Context Capture software to perform point cloud fusion and three-dimensional modeling to perform three-dimensional modeling; through laser point cloud registration and absolute orientation, laser point cloud data and air-to-three encrypted point clouds are brought into the same coordinate system, 2 types of point clouds can be fused in a Context Capture, a triangular net is constructed on the basis of point cloud, the triangular net is smoothed and simplified, and finally, textures are automatically mapped on the basis of oblique photography images, so that a high-precision three-dimensional model can be generated.
The step of point cloud registration is particularly important in this process, and point cloud registration can be actually understood as: perfect coordinate transformation parameters are obtained through calculation (similar to the calculation of seven parameters), and point cloud data under different viewing angles are uniformly integrated under an appointed coordinate system through rigid transformation such as rotation and translation. In popular terms: two point clouds to be registered can be completely superposed with each other through a position transformation such as rotational translation, so that the two point clouds belong to rigid transformation, namely the shape and the size are completely the same, but the coordinate positions are different. The point cloud registration is to find the coordinate position transformation relation between two point clouds.
The point cloud registration is a process of calculating the accurate mapping of the space geometry relationship between point cloud sets obtained in different modes, solving coordinate conversion parameters and carrying out rigid body transformation on a data set to be converted. The ICP algorithm is an iterative algorithm of global registration, and very high registration accuracy can be obtained. As shown in fig. 2 and 3, PR (red point cloud) and RB (blue point cloud) are two point sets, and the algorithm calculates how to translate and rotate PB so that PB and PR overlap as much as possible.
The essence of the ICP algorithm is the optimal matching based on the least squares method, which repeats the process of "determining a set of points for correspondence → computing the optimal rigid body transformation" until some convergence criterion is satisfied indicating a correct match.
Given：two corresponding point sets：
X＝{x1，...，xn}
P＝{p1，...，pn}
Wanted：translation tand rotation R that
minimizes the sum of the squared error：
Where xi and pi are corresponding points.
So if the correct two point correspondences are known, then the relative transformation (rotation, translation) between the two point sets can be used to solve the closed solution.
The centroids of the two sets of points x and P are first calculated, μ x and μ P, respectively.
Then, the corresponding centroid (centroid of mass from every point in the two point sets) is subtracted from the two point sets respectively
The resulting point sets are：
X′＝{xi-μx}＝{x′i}
and
P′＝{pi-μp}＝{p′i}
Optimization of the objective function E (R, t) is the last stage of the ICP algorithm. After the objective function is obtained, what method is adopted to make it converge to the minimum needs to be left for the software to automatically process. .
Based on the above, the ICP algorithm has the obvious advantages: 1. a very accurate registration effect can be obtained; 2. the segmentation and feature extraction of the processed point set are not needed; 3. under the condition of a better initial value, good algorithm convergence can be obtained.
The ICP algorithm also has its own disadvantages: 1. in the process of searching for the corresponding point, the calculation amount is very large, which is a bottleneck of the traditional ICP algorithm; 2. when the corresponding point is searched in the standard ICP algorithm, the point with the shortest Euclidean distance is considered as the corresponding point. This assumption is not reasonable and can result in a certain number of false corresponding points.
The feature-based multipoint cloud data registration process mainly has several aspects, one is significant feature point extraction, and geometric features or semantic features of all elements on the ground can be extracted; secondly, identifying homonymous features, and acquiring and identifying corresponding features; thirdly, solving a conversion parameter between the two; the fourth step is to use this parameter to perform a precise registration of the point cloud.
In the standard ICP algorithm, all points in the point set are selected to calculate corresponding points, and the number of elements of the point set used for registration is usually very large, and the time consumed for calculation through the point set is very long. In the research of experts on a global scale, various methods are proposed to select registration elements, and the main purpose of these methods is to reduce the number of elements of a point set, i.e. how to select the minimum points to represent all characteristic information of an original point set. When the point set is selected, the following steps can be performed: 1. selecting all points; 2. uniform sampling (Uniform sub-sampling); 3. random sampling (Random sampling); 4. feature based Sampling (Feature based Sampling); 5. normal-space sampling (Normal-space sampling), which, as shown in the following figure, ensures continuity in the Normal direction (applied which samples have been distributed as uniform as possible).
In the above, the point cloud registration based on the local features can be classified according to 3 kinds of feature primitives, such as points, lines and planes, and 3 steps of feature extraction, homonymy feature identification, calculation of conversion parameters and application are required. In a point cloud registration task oriented to urban three-dimensional modeling, a common method is to extract features by using various three-dimensional point cloud descriptors such as Fast Point Feature Histograms (FPFH), etc., or to identify geometric feature points and feature lines of building edges, because the geometric features of the building edges usually satisfy stability and specificity. The extraction of local features and the related point cloud index method, normal vector calculation and the like are hot research contents of researchers, and by utilizing the relation between points and lines and between points and surfaces, characteristic curves and building surfaces are extracted as features, so that better applicability and flexibility are provided for the feature-based registration method.
The problem of non-fusion may occur in the process of point cloud data fusion, so that the established three-dimensional model is not connected. Therefore, seamless integration of models established for oblique images and close-up images is required. When seamless integration is carried out, the models respectively established by the oblique image and the close-range image have the condition of staggered coverage, and the models established by the oblique image and the close-range image cannot be seamlessly connected by using the Context Capture software, so that the fitting position of the two models needs to be flattened in the DP-Modeller software, and the flattened tile and the fine tile in the close-range model are placed in a folder for calling.
And finding corresponding characteristic points in the point cloud generated by the tilt model and the point cloud collected by the laser radar by combining the technology to perform overall point cloud rough registration. Selecting obvious characteristic points for rough registration for tilt model data and three-dimensional laser point cloud data of the same region as follows. In order to facilitate the extraction of the characteristic points, a certain overlapping degree is required to be kept between the human-machine oblique image and the close-range image, and the specific setting of the overlapping degree is determined according to the condition of the task area. The overlapping degree refers to the overlapping degree of images kept between adjacent pictures or between adjacent flight paths when the airplane photographs along the flight paths. A overlap degree for three-dimensional reconstruction generally sets up to take (side direction) overlap degree more than 70%, and airline (vertical) overlap degree more than 80%, reaches the assurance of this overlap degree, all is the automatic setting that utilizes unmanned aerial vehicle flight controller software at present, only needs input unmanned aerial vehicle to carry on the parameter of oblique camera, and unmanned aerial vehicle flying height can design out the airline of guaranteeing the overlap degree. Referring to the schematic diagram of the calculation principle shown in fig. 9:
the camera is vertical to the long-edge flying direction, the route distance is x, and the dimension of the long edge of the sensor is used for calculating the sidewise overlapping degree, wherein d/ccd is h/len;
the side direction overlapping degree is x/len;
then, x is the side-by-side overlapping degree h/d ccd.
Referring to fig. 4-6, after a plurality of feature points are acquired, the two data sets are roughly registered by using a registration toolbar so that they can be corrected in the same system, and as shown in the drawings, the two data sets are basically overlapped.
After the rough registration model is obtained, the point clouds are subjected to fine registration correction by utilizing an ICP (inductively coupled plasma) algorithm, so that the point clouds are tightly attached together, and the requirement of producing a high-precision three-dimensional model is met. Referring to fig. 7 and 8, the following two graphs are compared to show that the fusion precision of the two graphs is high after ICP registration. Experimental research shows that although the three-dimensional model based on oblique photography has obvious distortion in a shielded area, the missing image point cloud can be repaired through the ground laser point cloud, and the fineness of the model can be obviously improved by the method based on the fusion modeling of the oblique photography and the ground laser point cloud.
The foregoing is illustrative of the preferred embodiments of the present invention only and is not to be construed as limiting the claims. The invention is not limited to the above embodiments, the specific construction of which allows variations, and in any case variations, which are within the scope of the invention as defined in the independent claims.
Claims (10)
1. A three-dimensional modeling method based on fusion of an inclined model and laser point cloud data is characterized by comprising the following steps:
acquiring a basic data source of three-dimensional modeling of a task target area through an oblique photogrammetry module and a three-dimensional laser scanning module, wherein the basic data source of three-dimensional modeling comprises an oblique image acquired by the oblique photogrammetry module and a close-range image acquired by the three-dimensional laser scanning module;
the data processing module establishes a model based on the oblique image to obtain an oblique model point cloud of a task target area; establishing a model based on the close-range image to obtain a laser point cloud of a task target area;
the data processing module acquires the tilt model point cloud and the laser point cloud, and performs coarse registration on the two data by using a registration tool so that the two data are corrected in the same system and overlapped to obtain a coarse registration model of a task target area;
after the data processing module obtains the rough registration model, a computer algorithm tool is used for carrying out precise registration correction on the point cloud so as to enable the point cloud to be closely attached together, and effective fusion of the point cloud of the tilt model and the point cloud data of the laser is completed;
the data processing module constructs a triangulation network on the basis of the effectively fused point cloud, meanwhile, the triangulation network is smoothed and simplified, and a high-precision three-dimensional model of a task target area is generated by automatically mapping textures on the basis of oblique photography images.
2. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 1, characterized in that: the oblique photography measurement module obtains the oblique image based on unmanned aerial vehicle oblique flight photography.
3. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 2, characterized in that: the data processing module comprises Context Capture software and fuses the tilt model point cloud and the laser point cloud in the Context Capture.
4. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 3, characterized in that: and performing coarse registration by adopting a characteristic point mode, selecting a plurality of obvious characteristic points of the tilt model point cloud and the laser point cloud, and performing coarse registration of the two data by utilizing a registration toolbar of the Context Capture.
5. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 4, characterized in that: and a certain overlapping degree is kept between the oblique image and the close-up image.
6. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 3, characterized in that: the data processing module comprises DP-Modeller software, when the tilt model point cloud and the laser point cloud are not fused to cause that the established three-dimensional models are not connected, the DP-Modeller software is utilized to carry out seamless integration on the models established by the tilt image and the close-range image, the overlapping position of the two models is stepped in the DP-Modeller software, and the stepped tiles and the fine tiles in the close-range model are placed in a folder to be called.
7. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 2, characterized in that: and an ICP algorithm is used for carrying out precise registration correction on the point cloud.
8. The three-dimensional modeling method based on the fusion of the tilt model and the laser point cloud data of claim 2, characterized in that: in the mapping texture, an image with the optimal resolution is selected from multiple source images for texture mapping so as to obtain a high-precision real three-dimensional model.
9. A three-dimensional modeling system based on fusion of an inclined model and laser point cloud data is characterized by comprising the following components:
the oblique photography measurement module is used for acquiring an oblique image of the three-dimensional modeling of the task target area;
the three-dimensional laser scanning module is used for acquiring a close-range image of a task target area three-dimensional modeling;
and the data processing module is used for reading, storing and processing the oblique photogrammetry module and the three-dimensional laser scanning module by an algorithm to obtain a basic data source for three-dimensional modeling of the task target area and output a high-precision three-dimensional model of the task target area.
10. The three-dimensional modeling system based on tilt model and laser point cloud data fusion of claim 9, characterized in that: the data processing module comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the three-dimensional modeling method based on fusion of the tilt model and the laser point cloud data according to any one of claims 1 to 8 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110311937.0A CN112927360A (en) | 2021-03-24 | 2021-03-24 | Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110311937.0A CN112927360A (en) | 2021-03-24 | 2021-03-24 | Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112927360A true CN112927360A (en) | 2021-06-08 |
Family
ID=76175745
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110311937.0A Pending CN112927360A (en) | 2021-03-24 | 2021-03-24 | Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112927360A (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113345084A (en) * | 2021-06-29 | 2021-09-03 | 吉林省交通规划设计院 | Three-dimensional modeling system and three-dimensional modeling method |
| CN113393507A (en) * | 2021-06-30 | 2021-09-14 | 南京工业大学 | Unmanned aerial vehicle point cloud and ground three-dimensional laser scanner point cloud registration method |
| CN113628341A (en) * | 2021-08-31 | 2021-11-09 | 时空云科技有限公司 | Automatic single modeling method based on oblique photography data and LIDAR point cloud fusion |
| CN113643434A (en) * | 2021-07-12 | 2021-11-12 | 广东省国土资源测绘院 | Three-dimensional modeling method based on air-ground cooperation, intelligent terminal and storage device |
| CN113963047A (en) * | 2021-11-02 | 2022-01-21 | 株洲市规划设计院 | Mobile phone image real scene based refined modeling local quick updating method |
| CN114186603A (en) * | 2021-09-28 | 2022-03-15 | 北京博能科技股份有限公司 | Multi-source model single fusion method and system, storage medium and terminal |
| CN114463521A (en) * | 2022-01-07 | 2022-05-10 | 武汉大学 | Building target point cloud rapid generation method for air-ground image data fusion |
| CN114623804A (en) * | 2022-03-11 | 2022-06-14 | 浙江泰乐地理信息技术股份有限公司 | Oblique photography measurement method and measurement system based on multi-point laser assistance |
| CN115311434A (en) * | 2022-10-10 | 2022-11-08 | 深圳大学 | Tree three-dimensional reconstruction method and device based on oblique photography and laser data fusion |
| CN115564661A (en) * | 2022-07-18 | 2023-01-03 | 武汉大势智慧科技有限公司 | Automatic restoration method and system for building glass area vertical face |
| CN116168188A (en) * | 2023-03-17 | 2023-05-26 | 易智瑞信息技术有限公司 | Fusion method, system and equipment for large-range inclined three-dimensional data |
| CN116448080A (en) * | 2023-06-16 | 2023-07-18 | 西安玖安科技有限公司 | Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method |
| CN115908706B (en) * | 2022-11-15 | 2023-08-08 | 中国铁路设计集团有限公司 | High-speed railway completion acceptance method with fusion of live three-dimensional model and image |
| CN116630598A (en) * | 2023-07-19 | 2023-08-22 | 齐鲁空天信息研究院 | Visual positioning method and device under large scene, electronic equipment and storage medium |
| CN116645485A (en) * | 2023-06-02 | 2023-08-25 | 中交一公局第二工程有限公司 | Ancient building model construction method based on unmanned aerial vehicle oblique photography |
| CN116740288A (en) * | 2023-08-14 | 2023-09-12 | 北京数慧时空信息技术有限公司 | Three-dimensional reconstruction method integrating laser radar and oblique photography |
| CN116895022A (en) * | 2023-09-11 | 2023-10-17 | 广州蓝图地理信息技术有限公司 | Building boundary extraction method based on point cloud data processing |
| CN117091573A (en) * | 2023-08-22 | 2023-11-21 | 苏州庄舟智能科技有限公司 | Full-model integrated system for shore road |
| CN117218743A (en) * | 2023-11-07 | 2023-12-12 | 诺比侃人工智能科技(成都)股份有限公司 | Intelligent inspection control method and system based on machine vision |
| CN117371949A (en) * | 2023-10-24 | 2024-01-09 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
| CN117591612A (en) * | 2024-01-19 | 2024-02-23 | 贵州北斗空间信息技术有限公司 | Method, device and system for loading terrain tile data on three-dimensional platform in real time |
| CN118015055A (en) * | 2024-04-08 | 2024-05-10 | 江苏狄诺尼信息技术有限责任公司 | Multi-source survey data fusion processing method and system based on depth fusion algorithm |
| CN117371949B (en) * | 2023-10-24 | 2024-05-31 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107064954A (en) * | 2017-05-24 | 2017-08-18 | 云南省交通规划设计研究院 | A kind of highway topography mapping method and system based on vehicle-mounted and airborne cloud |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN112017227A (en) * | 2020-08-27 | 2020-12-01 | 上海交通建设总承包有限公司 | Method for hybrid visualization of terrain model and tidal data generated by point cloud fusion |
-
2021
- 2021-03-24 CN CN202110311937.0A patent/CN112927360A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107064954A (en) * | 2017-05-24 | 2017-08-18 | 云南省交通规划设计研究院 | A kind of highway topography mapping method and system based on vehicle-mounted and airborne cloud |
| CN111458720A (en) * | 2020-03-10 | 2020-07-28 | 中铁第一勘察设计院集团有限公司 | Airborne laser radar data-based oblique photography modeling method for complex mountainous area |
| CN112017227A (en) * | 2020-08-27 | 2020-12-01 | 上海交通建设总承包有限公司 | Method for hybrid visualization of terrain model and tidal data generated by point cloud fusion |
Non-Patent Citations (2)
| Title |
|---|
| 李娜: ""基于多源影像数据融合的三维实景重建及应用研究"", 《硕士电子期刊》, vol. 2019, no. 09, pages 201 - 213 * |
| 顾建祥 等: "《基于地理实体的全息要素采集与建库》", 上海科学技术出版社, pages: 14 - 46 * |
Cited By (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113345084A (en) * | 2021-06-29 | 2021-09-03 | 吉林省交通规划设计院 | Three-dimensional modeling system and three-dimensional modeling method |
| CN113393507B (en) * | 2021-06-30 | 2023-07-28 | 南京工业大学 | Unmanned aerial vehicle point cloud and ground three-dimensional laser scanner point cloud registration method |
| CN113393507A (en) * | 2021-06-30 | 2021-09-14 | 南京工业大学 | Unmanned aerial vehicle point cloud and ground three-dimensional laser scanner point cloud registration method |
| CN113643434A (en) * | 2021-07-12 | 2021-11-12 | 广东省国土资源测绘院 | Three-dimensional modeling method based on air-ground cooperation, intelligent terminal and storage device |
| CN113628341A (en) * | 2021-08-31 | 2021-11-09 | 时空云科技有限公司 | Automatic single modeling method based on oblique photography data and LIDAR point cloud fusion |
| CN113628341B (en) * | 2021-08-31 | 2023-12-19 | 珠海尚方清洁能源科技有限公司 | Automatic monomer modeling method based on oblique photographic data and LIDAR point cloud fusion |
| CN114186603A (en) * | 2021-09-28 | 2022-03-15 | 北京博能科技股份有限公司 | Multi-source model single fusion method and system, storage medium and terminal |
| CN113963047A (en) * | 2021-11-02 | 2022-01-21 | 株洲市规划设计院 | Mobile phone image real scene based refined modeling local quick updating method |
| CN114463521A (en) * | 2022-01-07 | 2022-05-10 | 武汉大学 | Building target point cloud rapid generation method for air-ground image data fusion |
| CN114463521B (en) * | 2022-01-07 | 2024-01-30 | 武汉大学 | Building target point cloud rapid generation method for air-ground image data fusion |
| CN114623804A (en) * | 2022-03-11 | 2022-06-14 | 浙江泰乐地理信息技术股份有限公司 | Oblique photography measurement method and measurement system based on multi-point laser assistance |
| CN115564661A (en) * | 2022-07-18 | 2023-01-03 | 武汉大势智慧科技有限公司 | Automatic restoration method and system for building glass area vertical face |
| CN115564661B (en) * | 2022-07-18 | 2023-10-10 | 武汉大势智慧科技有限公司 | Automatic repairing method and system for building glass area elevation |
| CN115311434A (en) * | 2022-10-10 | 2022-11-08 | 深圳大学 | Tree three-dimensional reconstruction method and device based on oblique photography and laser data fusion |
| CN115908706B (en) * | 2022-11-15 | 2023-08-08 | 中国铁路设计集团有限公司 | High-speed railway completion acceptance method with fusion of live three-dimensional model and image |
| CN116168188A (en) * | 2023-03-17 | 2023-05-26 | 易智瑞信息技术有限公司 | Fusion method, system and equipment for large-range inclined three-dimensional data |
| CN116168188B (en) * | 2023-03-17 | 2024-02-02 | 易智瑞信息技术有限公司 | Fusion method, system and equipment for large-range inclined three-dimensional data |
| CN116645485B (en) * | 2023-06-02 | 2024-02-27 | 中交一公局第二工程有限公司 | Ancient building model construction method based on unmanned aerial vehicle oblique photography |
| CN116645485A (en) * | 2023-06-02 | 2023-08-25 | 中交一公局第二工程有限公司 | Ancient building model construction method based on unmanned aerial vehicle oblique photography |
| CN116448080B (en) * | 2023-06-16 | 2023-09-05 | 西安玖安科技有限公司 | Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method |
| CN116448080A (en) * | 2023-06-16 | 2023-07-18 | 西安玖安科技有限公司 | Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method |
| CN116630598A (en) * | 2023-07-19 | 2023-08-22 | 齐鲁空天信息研究院 | Visual positioning method and device under large scene, electronic equipment and storage medium |
| CN116630598B (en) * | 2023-07-19 | 2023-09-29 | 齐鲁空天信息研究院 | Visual positioning method and device under large scene, electronic equipment and storage medium |
| CN116740288B (en) * | 2023-08-14 | 2023-10-20 | 北京数慧时空信息技术有限公司 | Three-dimensional reconstruction method integrating laser radar and oblique photography |
| CN116740288A (en) * | 2023-08-14 | 2023-09-12 | 北京数慧时空信息技术有限公司 | Three-dimensional reconstruction method integrating laser radar and oblique photography |
| CN117091573A (en) * | 2023-08-22 | 2023-11-21 | 苏州庄舟智能科技有限公司 | Full-model integrated system for shore road |
| CN116895022B (en) * | 2023-09-11 | 2023-12-01 | 广州蓝图地理信息技术有限公司 | Building boundary extraction method based on point cloud data processing |
| CN116895022A (en) * | 2023-09-11 | 2023-10-17 | 广州蓝图地理信息技术有限公司 | Building boundary extraction method based on point cloud data processing |
| CN117371949A (en) * | 2023-10-24 | 2024-01-09 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
| CN117371949B (en) * | 2023-10-24 | 2024-05-31 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
| CN117218743B (en) * | 2023-11-07 | 2024-02-09 | 诺比侃人工智能科技(成都)股份有限公司 | Intelligent inspection control method and system based on machine vision |
| CN117218743A (en) * | 2023-11-07 | 2023-12-12 | 诺比侃人工智能科技(成都)股份有限公司 | Intelligent inspection control method and system based on machine vision |
| CN117591612A (en) * | 2024-01-19 | 2024-02-23 | 贵州北斗空间信息技术有限公司 | Method, device and system for loading terrain tile data on three-dimensional platform in real time |
| CN117591612B (en) * | 2024-01-19 | 2024-04-09 | 贵州北斗空间信息技术有限公司 | Method, device and system for loading terrain tile data on three-dimensional platform in real time |
| CN118015055A (en) * | 2024-04-08 | 2024-05-10 | 江苏狄诺尼信息技术有限责任公司 | Multi-source survey data fusion processing method and system based on depth fusion algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112927360A (en) | Three-dimensional modeling method and system based on fusion of tilt model and laser point cloud data | |
| Bosché | Plane-based registration of construction laser scans with 3D/4D building models | |
| US7509241B2 (en) | Method and apparatus for automatically generating a site model | |
| CN103744086B (en) | A kind of high registration accuracy method of ground laser radar and close-range photogrammetry data | |
| CN106485690A (en) | Cloud data based on a feature and the autoregistration fusion method of optical image | |
| Barazzetti et al. | True-orthophoto generation from UAV images: Implementation of a combined photogrammetric and computer vision approach | |
| Hoegner et al. | Evaluation of methods for coregistration and fusion of rpas-based 3d point clouds and thermal infrared images | |
| CN113192200B (en) | Method for constructing urban real scene three-dimensional model based on space-three parallel computing algorithm | |
| Jin et al. | An indoor location-based positioning system using stereo vision with the drone camera | |
| Crombez et al. | 3D point cloud model colorization by dense registration of digital images | |
| CN112767461A (en) | Automatic registration method for laser point cloud and sequence panoramic image | |
| Alsadik | Guided close range photogrammetry for 3D modelling of cultural heritage sites | |
| Özdemir et al. | A multi-purpose benchmark for photogrammetric urban 3D reconstruction in a controlled environment | |
| Barazzetti et al. | Automated and accurate orientation of complex image sequences | |
| Kurazume et al. | Mapping textures on 3D geometric model using reflectance image | |
| Barazzetti et al. | Combined use of photogrammetric and computer vision techniques for fully automated and accurate 3D modeling of terrestrial objects | |
| Choi et al. | Precise geometric registration of aerial imagery and LIDAR data | |
| Moussa et al. | Complementing TLS point clouds by dense image matching | |
| Wu | Photogrammetry: 3-D from imagery | |
| CN116563377A (en) | Mars rock measurement method based on hemispherical projection model | |
| Previtali et al. | An automatic multi-image procedure for accurate 3D object reconstruction | |
| Previtali et al. | Multi-step and multi-photo matching for accurate 3D reconstruction | |
| Remondino | Accurate and detailed image-based 3D documentation of large sites and complex objects | |
| Gao et al. | Vehicle-borne multi-sensor temporal–spatial pose globalization via cross-domain data association | |
| Zhang et al. | Point cloud registration with 2D and 3D fusion information on mobile robot integrated vision system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |