CN116524109B - WebGL-based three-dimensional bridge visualization method and related equipment - Google Patents
WebGL-based three-dimensional bridge visualization method and related equipment Download PDFInfo
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Abstract
The invention provides a three-dimensional bridge visualization method and related equipment based on WebGL, wherein the method comprises the following steps: acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge; acquiring effective characteristic data of the point cloud data based on the type of the target bridge; determining a modeling mode of the target bridge according to the type of the effective characteristic data; and acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data. The modeling data can be more accurate and objective based on three dimensions by acquiring the three-dimensional point cloud data of the target bridge, the authenticity of a modeling result can be improved, the calculation amount can be reduced, the modeling difficulty is reduced, the modeling efficiency is improved, and the practicability and convenience of the bridge three-dimensional modeling method can be further improved by screening the effective characteristic data in the point cloud data and determining the modeling mode according to the type of the effective characteristic data.
Description
Technical Field
The invention relates to the technical field of bridge engineering, in particular to a three-dimensional bridge visualization method based on WebGL and related equipment.
Background
When the visual modeling is carried out on the three-dimensional bridge, the current modeling method is generally to load a two-dimensional CAD base map, firstly draw a plane base map on the basis, and then construct a three-dimensional model. In the process of drawing the plane base map, the CAD base map is drawn according to real measurement data, and the plane base map is drawn according to the CAD base map, so that the position of the base plane in the modeling process is correct and reliable, however, in the process of building an elevation surface, the height is not determined according to the real data, so that the difference between the finally obtained three-dimensional model and a real bridge is large, and the practicability and accuracy of the bridge modeling method are affected.
Disclosure of Invention
The invention provides a three-dimensional bridge visualization method based on WebGL and related equipment, which are used for solving the problems that the current bridge modeling method is based on a two-dimensional base map for modeling, so that the objectivity of the height data of a model is poor, the gap between the three-dimensional model and a real bridge is large, and the practicability and the accuracy of the bridge modeling method are affected.
In a first aspect, the present invention provides a WebGL-based three-dimensional bridge visualization method, including:
acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge;
acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
determining a modeling mode of the target bridge according to the type of the effective characteristic data;
and acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data.
Optionally, the point cloud data includes: at least one of coordinate information, color information, and reflection intensity information of a plurality of scanning points on the target bridge.
Optionally, before the step of acquiring the valid feature data of the point cloud data based on the type of the target bridge, the method further includes:
acquiring the relative position relation between measuring stations to which the point cloud data belong;
and acquiring splicing data of the target bridge according to the relative position relation and the point cloud data.
Optionally, after the step of obtaining the splicing data of the target bridge according to the relative positional relationship and the point cloud data, the method further includes:
acquiring the noise distribution condition of the spliced data;
determining a denoising method of the spliced data based on the noise point distribution condition;
and denoising the spliced data by the denoising method to obtain denoising data.
Optionally, after the step of denoising the spliced data by the denoising method to obtain denoised data, the method further includes:
acquiring the region repetition condition of the denoising data;
removing the denoising data of the repeated area under the condition that the repeated area ratio of the denoising data is larger than the preset repeated area ratio, and obtaining first denoising data;
acquiring the data density of the first redundancy-removed data;
and under the condition that the data density of the first redundancy elimination data is larger than the preset density, the first redundancy elimination data is thinned and simplified, and second redundancy elimination data is obtained.
Optionally, the acquiring the valid feature data of the point cloud data based on the type of the target bridge includes:
acquiring the structural complexity of the target bridge;
under the condition that the structural complexity of the target bridge is smaller than or equal to the preset complexity, the effective characteristic data are characteristic point data and/or characteristic line data;
and under the condition that the structural complexity of the target bridge is greater than the preset complexity, the effective characteristic data are characteristic line data.
Optionally, before the step of acquiring the point cloud data of the target bridge, the method further includes:
determining the sampling density of the point cloud data according to the target precision of the three-dimensional modeling result and the environment of the target bridge;
an acquisition range of a single station is determined based on the registration requirements of the point cloud data.
In a second aspect, the present invention also provides a WebGL-based three-dimensional bridge visualization apparatus, including:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring point cloud data of a target bridge, and the point cloud data are three-dimensional data of the target bridge;
the second acquisition unit is used for acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
the determining unit is used for determining a modeling mode of the target bridge according to the type of the effective characteristic data;
and the modeling unit is used for acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data.
In a third aspect, the present invention also provides an electronic device, including a memory, and a processor, where the processor is configured to implement the steps of the WebGL-based three-dimensional bridge visualization method according to any one of the first aspect when executing a computer program stored in the memory.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the WebGL-based three-dimensional bridge visualization method according to any one of the first aspects above.
As can be seen from the above technical solutions, the present invention provides a WebGL-based three-dimensional bridge visualization method and related devices, the method comprising: acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge; acquiring effective characteristic data of the point cloud data based on the type of the target bridge; determining a modeling mode of the target bridge according to the type of the effective characteristic data; and acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data. The existing bridge modeling method is based on a two-dimensional base map, so that the height data of the model cannot be accurately obtained, the objectivity of the height data of the model is poor, and further the problem that the gap between the three-dimensional model and a real bridge is large and the practicability and the accuracy of the bridge modeling method are affected is caused. According to the method and the device, the modeling data can be more accurate and objective based on three-dimensional modeling by acquiring the three-dimensional point cloud data of the target bridge, the authenticity of a modeling result can be improved, the calculation amount can be reduced, the modeling difficulty is reduced, the modeling efficiency is improved, and the practicability and the convenience of the bridge three-dimensional modeling method can be further improved through screening the effective characteristic data in the point cloud data and determining the modeling mode according to the type of the effective characteristic data.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a three-dimensional bridge visualization method based on WebGL according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a WebGL-based three-dimensional bridge visualization device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The embodiments described in the examples below do not represent all embodiments consistent with the present application. Merely as examples of systems and methods consistent with some aspects of the present application as detailed in the claims. In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners, and the apparatus embodiments described below are merely exemplary.
As shown in fig. 1, an embodiment of the present application provides a three-dimensional bridge visualization method based on WebGL, including:
step S110, obtaining point cloud data of a target bridge, wherein the point cloud data are three-dimensional data of the target bridge.
For example, the target bridge may be scanned by a three-dimensional laser scanner to obtain the above-described point cloud data.
And step S120, acquiring effective characteristic data of the point cloud data based on the type of the target bridge.
By way of example, the type of the target bridge may be determined based on information such as the structure and color of the target bridge. The effective feature data may include feature point data, feature line data, feature plane data, and other feature data, and the effective feature data may be data that can represent an effective feature of the target bridge in the point cloud data.
Step S130, determining the modeling mode of the target bridge according to the type of the effective characteristic data.
Exemplary, the modeling means include: modeling based on a triangle network, modeling based on a point structure line structure plane mode, modeling based on a feature extraction mode, modeling based on multi-source data fusion and the like. The modeling mode of the target bridge may be determined to be a mode of constructing a line and a plane based on the type of the effective feature data, for example, in the case where the effective feature data is feature point data, based on the information such as the curvature of the feature point. And the modeling mode of the target bridge can be determined according to the number of the effective characteristic data. For example, in the case where the number of effective feature data is small and irregular feature line data, the modeling manner of the target bridge may be determined to be based on triangle network modeling.
And step 140, obtaining a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data.
By acquiring the three-dimensional point cloud data of the target bridge, modeling is performed based on three dimensions, modeling data can be more accurate and objective, the authenticity of a modeling result can be improved, the calculation amount can be reduced, the modeling mode can be rapidly determined by screening effective characteristic data in the point cloud data and determining the modeling mode according to the type of the effective characteristic data, modeling difficulty is reduced, modeling efficiency is improved, and the practicability and convenience of the bridge three-dimensional modeling method can be further improved.
According to some embodiments, the point cloud data includes: at least one of coordinate information, color information and reflection intensity information of a plurality of scanning points on the target bridge.
The coordinate information may be obtained by solving information such as a distance between the laser beam emitted from the three-dimensional laser scanner and the target scanning point, a relative angle in a horizontal direction, and a relative angle in a vertical direction. The color information may be obtained by an image acquisition device, and may correspond a position of a scanning point to a pixel position of an image, and assign color information on the pixel position to the point cloud data, where the color information may include a color value of the pixel. The reflection intensity information may be an echo intensity detected by a receiving device of the three-dimensional laser scanner, and the reflection intensity information may be related to factors such as a material of the object to be measured, an incident angle of laser light, and a laser wavelength of the instrument, and the reflection intensity of the scanning point may be determined based on the factors and the echo intensity, and the reflection intensity information may be assigned to the point cloud data.
Exemplary formats of the point cloud data include xyz, acs, stl, pts, las, which can determine the format of the point cloud data according to model information of the three-dimensional laser scanner, and can convert the point cloud data into a uniform format before processing the point cloud data according to a data format supported by the point cloud data processing software and the format of the point cloud data.
By collecting the coordinate information, the color information and the reflection intensity information of a plurality of scanning points on the target bridge, the shape, the color distribution and the material distribution of the target bridge can be determined, the three-dimensional modeling is carried out on the target bridge based on the point cloud information formed by the information, the authenticity of a three-dimensional modeling result can be improved, the modeling efficiency and the modeling quality can be improved, and the practicability of the bridge three-dimensional modeling method can be further improved.
According to some embodiments, before the step of acquiring the valid characteristic data of the point cloud data based on the type of the target bridge, the method further includes:
acquiring the relative position relation between measuring stations to which the point cloud data belong;
and acquiring splicing data of the target bridge according to the relative position relationship and the point cloud data.
The relative positional relationship is exemplified by a relative positional relationship between stations corresponding to point cloud data at the same scanning point included in different point cloud data. The mapping relation of the point cloud data between the measuring stations between two coordinate systems can be determined according to the relative position relation, and the point cloud data collected by different measuring stations can be located under the same coordinate system through the mapping relation so as to achieve the splicing of the point cloud data. And acquiring splicing data of the target bridge according to the mapping relation and the point cloud data.
For example, the same scan point may be determined in different point cloud data, then a rotation matrix and a translation matrix between different coordinate systems where different point cloud data are located may be determined according to the scan point, and different point cloud data may be spliced into the same coordinate system according to the rotation matrix and the translation matrix. For example, a three-dimensional laser scanner is respectively arranged at the two measuring stations A and B to scan the same object. For the same point, the scanning at the A position is the P point in the coordinate system, the scanning at the B position is the Q point in the coordinate system, and for the point clouds P and Q acquired by two stations, P is i (X,Y,Z)∈P，Q i (X, Y, Z) ∈Q and P i And Q i The coordinates of the same object point N in different coordinate systems can be obtained, so that two sets of transformation relations (R, T):
the rotation matrix R can be obtained according to equation (1):
the translation matrix T can also be derived from equation (1):
wherein alpha, beta and gamma are rotation angles along X, Y, Z axis, t x 、t y 、t z Is the translation amount.
By way of example, all point cloud data can be converted into the same coordinate system according to the rotation matrix R and the translation matrix T by means of target stitching, point cloud direct stitching, control point stitching and the like. Under the condition that the precision of the spliced data obtained by the splicing method is lower than the preset splicing precision, an error function E of the rotation matrix R and the translation vector t can be obtained:
equation (4) may be solved to minimize the error function E to obtain the optimal coordinate transformation matrix.
Because the angle that the three-dimensional laser scanner can scan is limited and the influence of the shielding object on the scanning result can exist in the scanning range of the scanner, the three-dimensional laser scanner can only scan partial point cloud data of the target bridge and cannot cover the whole target bridge. In particular, multiple scans are needed to improve the scanning accuracy in places with large surface changes of the target object, so that the scanning quality and the scanning integrity of the target bridge can be ensured by scanning from multiple directions and different view angles. However, each measuring station has an independent coordinate system, so that the coordinate system conversion relation among different measuring stations is established by the method, the splicing quality of point cloud data can be ensured, the accuracy and the integrity of the splicing data are improved, the three-dimensional modeling of a target bridge is facilitated, the modeling efficiency and the modeling quality can be improved, and the practicability of the bridge three-dimensional modeling method can be further improved.
According to some embodiments, after the step of obtaining the splicing data of the target bridge according to the relative positional relationship and the point cloud data, the method further includes:
acquiring the noise distribution condition of the spliced data;
determining a denoising method of the spliced data based on the noise point distribution condition;
denoising the spliced data by the denoising method to obtain denoising data.
Illustratively, the noise distribution condition includes information such as distribution density of noise, cause of noise, and the like. The interest creating method comprises an outlier denoising method, a smooth denoising method and a statistical denoising method. For example, in the case where there is an isolated noise point which exists and has a low density in a unit range, and the distance from the point to the scanning center is greatly different from the distance from the other most points to the scanning center, it can be said that the point is an isolated point caused by airborne dust or the spot size or the like, and an outlier denoising method can be adopted. When in measurement, errors of the instrument or fluctuation points caused by shielding of external objects exist, and a smooth denoising method can be adopted. The point cloud data comprises effective points and ineffective points, wherein a statistical denoising method can be adopted under the condition that the effective points account for most and the ineffective points account for few.
In the process of acquiring point cloud data by using a three-dimensional laser scanner, the obtained data are affected by scanning equipment, surrounding environment, artificial disturbance and even the surface material of a scanned object, and noise exists more or less, so that the data cannot accurately express the spatial position of the scanned object. Therefore, the method for denoising the spliced data can realize the simplification of point cloud data, reduce the data processing amount, reduce modeling errors caused by noise points, improve the authenticity of modeling results, improve the modeling efficiency and modeling quality, and further improve the practicability of the bridge three-dimensional modeling method.
According to some embodiments, after the step of denoising the spliced data by the denoising method to obtain denoising data, the method further includes:
acquiring the region repetition condition of the denoising data;
removing the denoising data of the repeated area under the condition that the duty ratio of the repeated area of the denoising data is larger than a preset repeated duty ratio, and obtaining first denoising data;
acquiring the data density of the first redundancy elimination data;
and under the condition that the data density of the first redundancy elimination data is larger than the preset density, the first redundancy elimination data is thinned and simplified, and second redundancy elimination data is obtained.
The three-dimensional laser scanner can acquire massive point cloud data in a short time, and the data scanned by an object with high precision requirements are larger, so that massive data occupy a large amount of space of a system in data processing, the processing speed is reduced, the operation efficiency is low, the system is crashed and the like, and the data volume of the point cloud data needs to be reduced under the condition that the precision is not affected, and effective information is extracted. After data splicing, most of data in the repeated area is useless data, so that the modeling speed and quality are greatly influenced, and the point cloud data of the corresponding part can be directly removed. The thinning simplification means that the scanned data is overlarge in density and excessive in quantity, and a part of data is not very useful for later modeling, so that the data is simplified on the premise of meeting a certain precision and ensuring that the geometric characteristics of the measured object are not influenced, and the operation speed, the modeling efficiency and the model precision of the data are improved. Therefore, the method can be used for screening the data, so that the processing speed of the point cloud data can be improved, the processing amount of the data is reduced, the modeling precision is improved, the modeling efficiency is improved, and the practicability of the modeling method is improved.
According to some embodiments, the obtaining the valid feature data of the point cloud data based on the type of the target bridge includes:
acquiring the structural complexity of the target bridge;
under the condition that the structural complexity of the target bridge is smaller than or equal to the preset complexity, the effective characteristic data are characteristic point data and/or characteristic line data;
and under the condition that the structural complexity of the target bridge is greater than the preset complexity, the effective characteristic data are characteristic line data.
For example, in the case where the effective feature data is feature point data, the feature point data may be acquired by a curvature estimation method or a principal component analysis method. Under the condition that the effective characteristic data is characteristic line data, the characteristic line data can be obtained through a characteristic line fitting method.
Illustratively, in the process of acquiring feature point data by using curvature estimation method, a local coordinate system is first established to obtain p i (x i ,y i ,z i ) Point(s)Centered on the w axis and p i The normal vector directions of the point tangent planes are consistent, the u, v and w axes are orthogonal to form a rectangular coordinate system, and the u and v axes are p i The tangent plane of the point. The matrix T may be transformed by the origin:
converting origin of original coordinate system into p i The dots can be obtained by rotating the matrix R x ：
Rotated about the x-axis by alpha, alpha being n (n x ,n y ,n z ) The angle formed by the projection and the z axis is projected on the yoz plane, wherein,
by rotating the matrix R y ：
Rotating beta angle around y axis, beta angle normal vector forms an included angle with the z axis after last rotation, wherein,
sinβ＝n x (11)
and converting, wherein the original coordinate z-axis is overlapped with the w-axis, and the converted x-axis and y-axis are u-axis and v-axis. The local surface may be approximated by a quadric, with the approximate relationship:
S(u,v)＝(u,v,ω(u,v)) (12)
in the case where the quadric is a secondary paraboloid, both e and f are 0. For vertex P i There is field P j ∈nbhd(p i )(j＝1,2,...k)P j (x j ,y j ,z j ) The coordinate value at the local coordinate is (u) j ,v j ,ω j ) The transformation matrix is:
will (u) j ,v j ,ω j ) Substituting the parameters into the formulas (12) and (13) to obtain the equation set to the parameters a, b and c of the best fit quadric surface when k is larger than 3. After the fitted quadric parametric equation is obtained, the principal curvature and principal direction of the parametric surface can be calculated by replacing the curvature of the sampling points with the curvature value of the fitted quadric. The Gaussian curvature can be obtained by multiplying two principal curvatures, the property of a point on a curved surface can be expressed according to the positive and negative of the Gaussian curvature, when K is more than 0, the point is an elliptic point, when K=0, the point is a parabolic point, and when K is less than 0, the point is a hyperbolic point. The average curvature can express the concave-convex performance of the curved surface, and the average curvature is calculated as the average value of the sum of the main curvatures of the beams.
Illustratively, fitting the feature points may be accomplished by B-spline lines to obtain feature line data. In the presence of n+1 control vertices d i (i=0, 1, ·, n) and node vector u i (i=0, 1, ·, in the case of n) the number of the elements, the k-order B spline curve is:
the basis functions can be obtained:
characteristic lines can be fitted through control points, and cubic B spline curves of nodes distributed equidistantly are obtained:
wherein,
u＝x-x j ,u∈[0,1](j＝i,i+1,i+2,i+3) (18)
and (3) making:
for interpolating n+1 number points p i (i=0, ·, n + 3), and satisfies the following:
S i (0)＝p i (i＝0,···,n+3) (20)
after being carried in, the method comprises the following steps of:
wherein p is i D is the actual control point i The control point is a theoretical control point, wherein the number of equations is n-2, and the number of unknowns is n. Thus, by boundary conditions:
S 0 (0)＝q 0 (22)
S n-3 (0)＝q n-3 (23)
it is possible to obtain:
and solving the equation to obtain the characteristic line data.
Under the condition that the structural complexity of the target bridge is smaller than or equal to the preset complexity, the target bridge can be considered to be a bridge with strong regularity, and modeling can be completed by collecting characteristic point data or characteristic line data in the point cloud data. Under the condition that the structural complexity of the target bridge is greater than the preset complexity, the target bridge can be considered to be a bridge with a relatively complex structure and relatively weak regularity, and the characteristic point data cannot say the characteristics of the target bridge, so that the characteristics of the target bridge need to be represented through the characteristic line data to complete modeling. By collecting the effective characteristic data according to the complexity of the target bridge, the working difficulty of collecting the effective characteristic data can be reduced, the modeling mode of the target bridge can be conveniently determined, and the convenience and the practicability of the three-dimensional modeling method can be improved.
According to some embodiments, before the step of obtaining the point cloud data of the target bridge, the method further includes:
determining the sampling density of the point cloud data according to the target precision of the three-dimensional modeling result and the environment of the target bridge;
the acquisition range of the single station is determined based on the registration requirements of the point cloud data.
For example, a detailed operation plan is formulated before field data acquisition to ensure that the data acquisition is performed smoothly, the sampling density of data points can be determined according to the precision required by a scanned object, the distance between an instrument and the scanned object can be set, the acquisition range of a single measuring station can be determined according to the distance between the instrument and the scanned object and the sampling density, and then the number of scanning stations can be set according to the acquisition range and the scanning route of a target bridge. Before data scanning, the survey area can be surveyed, the scanning sites are arranged according to the site conditions, the scanning area is ensured to be free of shielding objects as much as possible under the condition of ensuring the scanning precision, and the target object can be scanned in a large range as much as possible. The method can control the distance between the scanning measuring station and the target bridge to ensure the precision of point cloud data, reduce the generation of noise, control adjacent measuring stations to mutually scan three public targets to reduce the splicing difficulty, improve the splicing accuracy and save scanning resources. The targets can be uniformly distributed, so that the three targets are prevented from being collinear or coplanar during scanning, the target distribution can be ensured to be controlled within a certain incident angle, and no reflected signal caused by overlarge incident angle is avoided.
Before collecting point cloud data, determining the sampling density of the point cloud data according to the target precision of a three-dimensional modeling result and the interference degree of the environment of a target bridge on data collection, and determining the collection range of a single measuring station according to the registration requirement and the splicing difficulty of the point cloud data, so that the practicability of a scanning measuring station can be improved, the point cloud data redundancy or the occurrence of a scanning blind area can be avoided, the collection quality of the point cloud data can be improved, the scanning resources can be saved, the data processing amount can be reduced, the modeling efficiency can be further improved, and the practicability and the convenience of the three-dimensional bridge modeling method can be improved.
As shown in fig. 2, fig. 2 is a schematic structural diagram of a WebGL-based three-dimensional bridge visualization device according to an embodiment of the present application.
The embodiment of the application provides a three-dimensional bridge visualization device 200 based on WebGL, which comprises:
a first obtaining unit 201, configured to obtain point cloud data of a target bridge, where the point cloud data is three-dimensional data of the target bridge;
a second obtaining unit 202, configured to obtain valid feature data of the point cloud data based on the type of the target bridge;
a determining unit 203, configured to determine a modeling manner of the target bridge according to the type of the valid feature data;
and a modeling unit 204, configured to obtain a three-dimensional modeling result of the target bridge based on the modeling manner and the valid feature data.
The WebGL-based three-dimensional bridge visualization device 200 can implement each process implemented in the method embodiment of fig. 1, and in order to avoid repetition, a description thereof will be omitted.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
The embodiment of the present application provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320, wherein the processor 320 implements the following steps when executing the computer program 311:
acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge;
acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
determining a modeling mode of the target bridge according to the type of the effective characteristic data;
and acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data.
In a specific implementation, when the processor 320 executes the computer program 311, any implementation manner of the embodiment corresponding to fig. 1 may be implemented.
Since the electronic device described in this embodiment is a device for implementing an apparatus in this embodiment, based on the method described in this embodiment, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how to implement the method in this embodiment for this electronic device will not be described in detail herein, and as long as those skilled in the art implement the device for implementing the method in this embodiment for this application, all fall within the scope of protection intended by this application.
As shown in fig. 4, fig. 4 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.
The present embodiment provides a computer readable storage medium 400 having stored thereon a computer program 411, which computer program 411 when executed by a processor realizes the steps of:
acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge;
acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
determining a modeling mode of the target bridge according to the type of the effective characteristic data;
and acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present application also provide a computer program product comprising computer software instructions that, when run on a processing device, cause the processing device to perform a flow in a WebGL-based three-dimensional bridge visualization method as in the corresponding embodiment of fig. 1.
The computer program product described above includes one or more computer instructions. When the above-described computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from one website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above-described method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In summary, the above embodiments are only for illustrating the technical solution of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. The three-dimensional bridge visualization method based on WebGL is characterized by comprising the following steps of:
acquiring point cloud data of a target bridge, wherein the point cloud data is three-dimensional data of the target bridge;
acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
determining a modeling mode of the target bridge according to the type of the effective characteristic data;
acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data;
the obtaining the effective feature data of the point cloud data based on the type of the target bridge includes:
acquiring the structural complexity of the target bridge;
the effective characteristic data are characteristic point data under the condition that the structural complexity of the target bridge is smaller than or equal to the preset complexity;
under the condition that the structural complexity of the target bridge is greater than a preset complexity, the effective characteristic data are characteristic line data;
the determining the modeling mode of the target bridge according to the type of the effective characteristic data comprises the following steps:
under the condition that the effective characteristic data are the characteristic point data, determining that the modeling mode of the target bridge is a mode of constructing a line and a plane according to the curvature information of the characteristic points; or alternatively, the first and second heat exchangers may be,
and under the condition that the effective characteristic data are irregular characteristic line data and the quantity of the effective characteristic data is small, determining the modeling mode of the target bridge to be based on triangle network modeling.
2. The WebGL-based three-dimensional bridge visualization method of claim 1, wherein the point cloud data includes: at least one of coordinate information, color information, and reflection intensity information of a plurality of scanning points on the target bridge.
3. The WebGL-based three-dimensional bridge visualization method according to claim 1, further comprising, before the step of obtaining valid feature data of the point cloud data based on the type of the target bridge:
acquiring the relative position relation between measuring stations to which the point cloud data belong;
and acquiring splicing data of the target bridge according to the relative position relation and the point cloud data.
4. The WebGL-based three-dimensional bridge visualization method according to claim 3, further comprising, after the step of obtaining the stitching data of the target bridge according to the relative positional relationship and the point cloud data:
acquiring the noise distribution condition of the spliced data;
determining a denoising method of the spliced data based on the noise point distribution condition;
and denoising the spliced data by the denoising method to obtain denoising data.
5. The WebGL-based three-dimensional bridge visualization method according to claim 4, further comprising, after the step of denoising the spliced data by the denoising method, a step of obtaining denoising data:
acquiring the region repetition condition of the denoising data;
removing the denoising data of the repeated area under the condition that the repeated area ratio of the denoising data is larger than the preset repeated area ratio, and obtaining first denoising data;
acquiring the data density of the first redundancy-removed data;
and under the condition that the data density of the first redundancy elimination data is larger than the preset density, the first redundancy elimination data is thinned and simplified, and second redundancy elimination data is obtained.
6. The WebGL-based three-dimensional bridge visualization method according to claim 1, further comprising, before the step of obtaining the point cloud data of the target bridge:
determining the sampling density of the point cloud data according to the target precision of the three-dimensional modeling result and the environment of the target bridge;
an acquisition range of a single station is determined based on the registration requirements of the point cloud data.
7. A WebGL-based three-dimensional bridge visualization device, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring point cloud data of a target bridge, and the point cloud data are three-dimensional data of the target bridge;
the second acquisition unit is used for acquiring effective characteristic data of the point cloud data based on the type of the target bridge;
the determining unit is used for determining a modeling mode of the target bridge according to the type of the effective characteristic data;
the modeling unit is used for acquiring a three-dimensional modeling result of the target bridge based on the modeling mode and the effective characteristic data;
the obtaining the effective feature data of the point cloud data based on the type of the target bridge includes:
acquiring the structural complexity of the target bridge;
the effective characteristic data are characteristic point data under the condition that the structural complexity of the target bridge is smaller than or equal to the preset complexity;
under the condition that the structural complexity of the target bridge is greater than a preset complexity, the effective characteristic data are characteristic line data;
the determining the modeling mode of the target bridge according to the type of the effective characteristic data comprises the following steps:
under the condition that the effective characteristic data are the characteristic point data, determining that the modeling mode of the target bridge is a mode of constructing a line and a plane according to the curvature information of the characteristic points; or alternatively, the first and second heat exchangers may be,
and under the condition that the effective characteristic data are irregular characteristic line data and the quantity of the effective characteristic data is small, determining the modeling mode of the target bridge to be based on triangle network modeling.
8. An electronic device comprising a memory, a processor, wherein the processor is configured to implement the steps of the WebGL-based three-dimensional bridge visualization method of any one of claims 1 to 6 when executing a computer program stored in the memory.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the WebGL-based three-dimensional bridge visualization method of any one of claims 1 to 6.
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