CN110189400B - Three-dimensional reconstruction method, three-dimensional reconstruction system, mobile terminal and storage device - Google Patents
Three-dimensional reconstruction method, three-dimensional reconstruction system, mobile terminal and storage device Download PDFInfo
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Abstract
The invention provides a three-dimensional reconstruction method, a three-dimensional reconstruction system, a mobile terminal and a storage device, wherein the three-dimensional reconstruction method comprises the following steps: acquiring point cloud data (namely global point cloud data) of a first frame of three-dimensional measurement data, selecting a local area with an overlapping area in an area corresponding to the global point cloud data for measurement, acquiring the local point cloud data, registering and updating the global point cloud data, repeating the process until all surface areas are measured, and finally performing global optimization processing on the global point cloud data updated after the measurement is completed to obtain a point cloud model. The invention carries out three-dimensional reconstruction by a method of breaking the whole into parts, can obtain measurement data with high density and high precision, and has more advantages particularly for the three-dimensional measurement of large-size objects.
Description
Technical Field
The invention belongs to the field of three-dimensional sensing and measurement, and particularly relates to a three-dimensional reconstruction method, a three-dimensional reconstruction system, a mobile terminal and a storage device.
Background
In recent years, 3D industry is undergoing rapid development. From 3D measurement to 3D printing, an integrated industrial chain with unique characteristics is formed, and certain difficulty still exists in measurement and modeling of large-size objects (large fan blades, oil tanks, ship bodies, automobile covering parts, large sculptures and the like) at present, mainly embodied in that the size of a measured object is large, high-density data is difficult to obtain, and the high density and high precision of the data are difficult to guarantee simultaneously. Therefore, the prior art still needs to be developed.
Disclosure of Invention
The invention aims to provide a three-dimensional reconstruction method, a three-dimensional reconstruction system, a mobile terminal and a storage device, and aims to solve the problem that the existing measurement modeling method is difficult to ensure high density and high precision of data for large-size objects at the same time.
In order to solve the above technical problem, the present invention is implemented as a three-dimensional reconstruction method, including the steps of:
s1, calibrating a three-dimensional reconstruction coordinate system; acquiring point cloud data of a first frame of three-dimensional measurement data of a measured object, wherein the point cloud data is called global point cloud data, and a coordinate system based on the first frame of three-dimensional measurement data is called a global coordinate system;
s2, performing three-dimensional measurement on the surface of a certain local area of the measured object, performing three-dimensional reconstruction by using a binocular stereo vision principle to obtain point cloud data of the local area, wherein the point cloud data of the local area is called as local point cloud data, and an overlapping area exists between the local area and an area corresponding to the global point cloud data;
s3, transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area, and updating the global point cloud data;
s4, keeping the measured object still, changing a measurement view angle to measure the measured object, and repeating the steps S2 to S3 until the measured object is measured;
and S5, performing global optimization processing on the updated global point cloud data after the measurement is completed to obtain a point cloud model.
Further, the step S2 specifically includes the following steps:
s21, performing three-dimensional measurement on the surface of the local area to obtain a first speckle image and a second speckle image of the local area;
s22, determining a whole pixel level corresponding point of each pixel in the first speckle image in the second speckle image;
s23, searching sub-pixel corresponding points of the second speckle image according to the whole pixel level corresponding points and the coordinates of each pixel point in the first speckle image to obtain sub-pixel corresponding points in the second speckle image;
and S24, performing three-dimensional reconstruction by combining the binocular stereoscopic vision principle and the corresponding relation of the sub-pixels of the second speckle image to obtain local point cloud data of the surface of the measured object.
Further, the step S22 specifically includes the following steps:
step S221, selecting (2 w) with the same size for the first speckle image and the second speckle image according to a correlation calculation formula m +1)×(2w m + 1) performing a correlation operation on the pixel region, wherein the correlation calculation formula is as follows:
wherein, I L (u L ，v L ) Representing the grey value, I, of a first speckle image plane point (u, v) in the selected area R (u R ，v R ) Representing the grey value of a second speckle image plane point (u, v) in the selected area,
and &>
Respectively representing the gray level average value of the selected areas of the first speckle image and the second speckle image, omega represents the correlation coefficient, W m Representing a number of pixels;
step S222, selecting a corresponding point corresponding to the maximum value of the calculated value of the correlation coefficient and exceeding the set threshold as a corresponding point at the whole pixel level.
Further, the step S23 specifically includes the following steps:
creating a window size of(2w m +1)×(2w m + 1) reference sub-window;
taking a nonlinear spatial correlation function omega(s) under a second-order parallax model as a function to be optimized of Newton-Raphson iterative operation:
wherein the content of the first and second substances,
u d 、v d is zero order parallax and is greater or less than>
Is first order parallax, and>
for the second-order parallax, Δ u and Δ v are differences between other pixel points and the central pixel point in the first speckle image, and the second-order parallax model is as follows:
according to preset iteration step number and an iteration operation formula
Performing iterative operation to determine the correlation function value s calculated by the last iterative operation N Is the result value->
Wherein the value range of N is an integer greater than or equal to 1, and N represents a variable sNumber of(s) 0 And taking the position of the maximum value of the correlation coefficient obtained by correlation calculation of the integer pixel as an initial value, and calculating the corresponding point of the sub-pixel according to the result value and a second-order parallax model.
Further, in the step S3, the local point cloud data and the global point cloud data are registered according to the overlapping region, and the registration is performed by using a point-to-model method with a coarse-to-fine strategy based on an iterative closest point frame.
Further, in step S5, the optimizing the global point cloud data includes: and establishing an error evaluation function uniformly containing all the overlapping regions, and averagely distributing the registration error to each overlapping region through the solution of the error function so as to reduce the accumulation of the registration error.
A three-dimensional reconstruction system, comprising:
a three-dimensional measurement module to: calibrating the three-dimensional reconstruction coordinate system; acquiring point cloud data of a first frame of three-dimensional measurement data of a measured object, wherein the point cloud data is called global point cloud data, and a coordinate system based on the first frame of three-dimensional measurement data is called a global coordinate system;
the system is also used for carrying out three-dimensional measurement on the surface of a certain local area of a measured object, carrying out three-dimensional reconstruction by using a binocular stereo vision principle to obtain point cloud data of the local area, which is called as local point cloud data, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data;
the global point cloud data updating module is used for transforming the local point cloud data to the global coordinate system, registering the local point cloud data with the global point cloud data according to the overlapping area and updating the global point cloud data;
and the global point cloud data optimization module is used for carrying out global optimization processing on the global point cloud data obtained after the measurement is finished to obtain a point cloud model.
Furthermore, the three-dimensional reconstruction system is carried on an unmanned aerial vehicle platform for three-dimensional measurement
A mobile terminal, comprising: a processor, a memory communicatively connected to the processor, the memory storing a computer program for, when executed, implementing the three-dimensional reconstruction method as described above; the processor is configured to invoke the computer program in the memory to implement the three-dimensional reconstruction method as described above.
A storage device storing a computer program executable to implement a three-dimensional reconstruction method as described above.
Compared with the prior art, the invention has the beneficial effects that: the invention carries out three-dimensional reconstruction based on the principle of binocular stereo vision, firstly, point cloud data (namely global point cloud data) of first frame three-dimensional measurement data is obtained, then, a local area with an overlapping area in an area corresponding to the global point cloud data is selected for measurement, the local point cloud data is obtained, registration is carried out again, the global point cloud data is updated, and the process is repeated until the measurement of all surface areas is completed. The invention carries out three-dimensional reconstruction by a method of breaking the whole into parts, can obtain measurement data with high density and high precision, and has more advantages particularly for the three-dimensional measurement of large-size objects.
Drawings
Fig. 1 is a flowchart of an embodiment of a three-dimensional reconstruction method of the present invention.
FIG. 2 is a flowchart of an embodiment of obtaining local point cloud data according to the present invention.
Fig. 3 is a functional block diagram of an embodiment of a three-dimensional reconstruction system according to the present invention.
Fig. 4 is a communication schematic diagram of an embodiment of a three-dimensional reconstruction mobile terminal according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The embodiment of the three-dimensional reconstruction method provided by the invention has the flow shown in fig. 1, and comprises the following steps S1-S5:
s1, calibrating a three-dimensional reconstruction coordinate system; the method comprises the steps of obtaining point cloud data of a first frame of three-dimensional measurement data of a measured object, wherein the point cloud data is called global point cloud data, and a coordinate system based on the first frame of three-dimensional measurement data is called a global coordinate system.
Taking a photographing type three-dimensional measurement as an example for explanation, the three-dimensional reconstruction system comprises two cameras and a projection device to form an active dual-purpose three-dimensional measurement unit for reconstructing three-dimensional point cloud data in real time. The calibration of the three-dimensional reconstruction coordinate system comprises the calibration of two cameras (a left camera and a right camera), specifically, the internal parameters, the lens distortion parameters and the rigid transformation between the coordinate systems of the two cameras are obtained, in the step, the corresponding rigid transformation is obtained, and the calibration of the corresponding component is completed. In the calibration process, point cloud data of a first frame of three-dimensional measurement data of a measured object, namely global point cloud data, is obtained and used as a registration reference of subsequent three-dimensional measurement.
And S2, performing three-dimensional measurement on the surface of a certain local area of the measured object, performing three-dimensional reconstruction by using a binocular stereo vision principle, and obtaining point cloud data of the local area, which is called local point cloud data, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data.
Preferably, the surface of the measured object can be measured in three dimensions by adopting an unmanned aerial vehicle to carry on a three-dimensional test system. The traditional method of adopting industrial mechanical arm to assist can not measure the complete appearance of a large object, and the method of pasting mark points or constructing a measuring network can reduce the measuring efficiency and increase the measuring cost. Therefore, the preferable method adopts a three-dimensional measurement system based on the unmanned aerial vehicle platform to solve the problems, and the unmanned aerial vehicle platform is carried, so that the camera can flexibly capture the viewpoint which can not be captured by the common camera, and the three-dimensional point cloud data is richer. The three-dimensional reconstruction system based on the unmanned aerial vehicle platform can adopt an external triggering mode, so that the camera and the projection device can synchronously perform projection and acquisition operations, and errors of three-dimensional reconstruction are reduced. Specifically, the process of acquiring the point cloud data of the local area is shown in fig. 2, and includes the following steps S21 to S24:
and S21, performing three-dimensional measurement on the surface of the local area to obtain a first speckle image and a second speckle image of the local area.
Specifically, the left camera and the right camera are used for shooting the surface of the local area at different viewing angles respectively to obtain a left speckle image (a first speckle image) and a right speckle image (a second speckle image).
And S22, determining a whole-pixel-level corresponding point of each pixel in the first speckle image in the second speckle image. The specific method comprises steps S221 and S222.
Step S221, selecting (2 w) with the same size for the first speckle image and the second speckle image according to a correlation calculation formula m +1)×(2w m + 1) performing a correlation operation on the pixel region, wherein the correlation calculation formula is as follows:
wherein, I L (u L ，v L ) Representing the grey value, I, of a first speckle image plane point (u, v) in the selected area R (u R ，v R ) Representing the grey value of a second speckle image plane point (u, v) in the selected region,
and &>
Respectively representing the gray level average value of the selected areas of the first speckle image and the second speckle image, omega represents a correlation coefficient, W m Representing a certain number of pixels;
step S222, selecting a corresponding point corresponding to the maximum value of the calculated value of the correlation coefficient and exceeding the set threshold as a corresponding point at the whole pixel level.
In the step, the selected correlation coefficient needs to meet two conditions, wherein the first condition is that the calculated value exceeds a set threshold value; under the condition that the precondition one is satisfied, the correlation coefficient is the maximum value, and the point corresponding to the maximum value is taken as the point corresponding to the integer pixel level.
And S23, searching sub-pixel corresponding points of the second speckle image according to the whole pixel level corresponding points and the coordinates of all pixel points in the first speckle image to obtain the sub-pixel corresponding points in the second speckle image. Specifically, the sub-pixel corresponding point searching method is as follows:
creating a window size of (2 w) within the first speckle image m +1)×(2w m + 1) reference sub-window;
taking a nonlinear spatial correlation function omega(s) under a second-order parallax model as a function to be optimized of Newton-Raphson iterative operation:
wherein the content of the first and second substances,
u d 、v d is zero order parallax and is greater or less than>
Is first order parallax, and>
for the second-order parallax, Δ u and Δ v are differences between other pixel points and the central pixel point in the first speckle image, and the second-order parallax model is as follows:
according to preset iteration steps and according to an iteration operation formula
Performing iterative operation to determine the correlation function value s calculated by the last iterative operation N In order to obtain the result value of the image,
wherein the value range of N is an integer greater than or equal to 1, N represents the number of variables s, s 0 And taking the position of the maximum value of the correlation coefficient obtained by correlation calculation of the integer pixel as an initial value, and calculating the corresponding point of the sub-pixel according to the result value and a second-order parallax model.
And S24, performing three-dimensional reconstruction by combining the binocular stereoscopic vision principle and the corresponding relation of the sub-pixels of the second speckle image to obtain local point cloud data of the surface of the measured object.
According to the method, a speckle correlation method is adopted to perform correlation operation on a left image and a right image so as to determine the whole pixel level corresponding point of each pixel in the left image in the right image; performing sub-pixel corresponding point search operation on the right image according to the whole pixel level corresponding point, the spatial correlation function and the pixel point coordinates of each left image in the left image to obtain a sub-pixel corresponding point; and performing three-dimensional reconstruction by using a binocular stereoscopic vision principle and combining the sub-pixel corresponding relation to obtain point cloud data of a local area of the object to be measured. The method can realize the rapid three-dimensional reconstruction of a single image, and is particularly suitable for the dynamic measurement of a three-dimensional scene.
And S3, transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area, and updating the global point cloud data.
Specifically, local point cloud data and the global point cloud data are registered by taking the overlapping area as a reference point, and a point-to-model method of a coarse-to-fine strategy is adopted for registration based on an iterative closest point frame.
And S4, keeping the measured object still, changing a measuring visual angle to measure the measured object, and repeating the steps S2 to S3 until the measured object is measured.
When the next measurement area is selected after one local area is measured, it is necessary to ensure that an overlapping area exists between the current measurement area and the measured area, so as to perform subsequent registration with the last updated global point cloud data.
And S5, performing global optimization processing on the global point cloud data updated after the measurement is completed to obtain a point cloud model.
Specifically, the optimization processing of the global point cloud data includes: and establishing an error evaluation function uniformly containing all the overlapping regions, averagely distributing the registration error to each overlapping region through the solution of the error function, and reducing the accumulation of the registration error, so that the three-dimensional measurement model is more accurate.
The three-dimensional reconstruction method provided by the invention is based on the binocular stereo vision principle to carry out three-dimensional reconstruction, firstly point cloud data (namely global point cloud data) of first frame three-dimensional measurement data is obtained, then a local area with an overlapping area in an area corresponding to the global point cloud data is selected to carry out measurement, the local point cloud data is obtained, registration is carried out again, the global point cloud data is updated, and the process is repeated until the measurement of all surface areas is completed. The invention carries out three-dimensional reconstruction by a method of breaking the whole into parts, can obtain measurement data with high density and high precision, and has more advantages particularly for the three-dimensional measurement of large-size objects. Furthermore, aiming at each measuring node, the invention adopts a speckle projection correlation method to determine the whole pixel corresponding point of the left and right speckle images, realizes the sub-pixel corresponding point positioning by using a Newton-Raphson iterative optimization method, and finally realizes the single-node depth data reconstruction; aiming at the multi-viewpoint depth data, the invention realizes the matching and fusion of the multi-viewpoint depth data by using an iteration closest point method, and finally outputs three-dimensional reconstruction data. The three-dimensional reconstruction method can also obtain high-density and high-precision measurement data aiming at large-size objects, and flexible and stable three-dimensional measurement is realized.
The invention also provides a three-dimensional reconstruction system, comprising:
a three-dimensional measurement module 1 for: calibrating the three-dimensional reconstruction coordinate system based on the measured object to obtain a global coordinate system; and simultaneously acquiring point cloud data of the first frame of three-dimensional measurement data of the measured object under the global coordinate system, namely global point cloud data.
The device is also used for carrying out three-dimensional measurement on the surface of a certain local area of a measured object, carrying out three-dimensional reconstruction by using a binocular stereo vision principle, and obtaining point cloud data of the local area, namely local point cloud data, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data.
And the global point cloud data updating module 2 is used for transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area and updating the global point cloud data.
And the global point cloud data optimization module 3 is used for performing global optimization processing on the global point cloud data acquired after the measurement is completed to obtain a point cloud model.
The three-dimensional reconstruction system provided by the invention can acquire measurement data with high density and high precision, and has advantages particularly for three-dimensional measurement of large-size objects.
The present invention also provides a mobile terminal, as shown in fig. 2, the mobile terminal includes:
as shown in fig. 2, the mobile terminal includes: a processor (processor) 10, a memory (memory) 20, a communication Interface (Communications Interface) 30, and a bus 40; wherein the content of the first and second substances,
the processor 10, the memory 20 and the communication interface 30 complete mutual communication through the bus 40;
the communication interface 30 is used for information transmission between communication devices of the mobile terminal;
the processor 10 is configured to call the computer program in the memory 20 to execute the method provided by the above method embodiments, for example, including: s1, calibrating a three-dimensional reconstruction coordinate system based on a measured object to obtain a global coordinate system; acquiring point cloud data (namely global point cloud data) of a first frame of three-dimensional measurement data of the measured object under the global coordinate system; s2, performing three-dimensional measurement on the surface of a certain local area of the measured object, and performing three-dimensional reconstruction by using a binocular stereo vision principle to obtain point cloud data (namely local point cloud data) of the local area, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data; s3, transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area, and updating the global point cloud data; s4, keeping the measured object still, changing a measurement view angle to measure the measured object, and repeating the steps S2 to S3 until the measured object is measured; and S5, performing global optimization processing on the global point cloud data updated after the measurement is completed to obtain a point cloud model.
The invention also provides a storage device, wherein the storage device stores a computer program which can be executed to implement the three-dimensional reconstruction method
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A method of three-dimensional reconstruction, comprising the steps of:
s1, calibrating a three-dimensional reconstruction coordinate system; acquiring point cloud data of a first frame of three-dimensional measurement data of a measured object, wherein the point cloud data is called global point cloud data, and a coordinate system based on the first frame of three-dimensional measurement data is called a global coordinate system;
s2, performing three-dimensional measurement on the surface of a certain local area of the object to be measured, and performing three-dimensional reconstruction by using a binocular stereo vision principle to obtain point cloud data of the local area, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data;
s3, transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area, and updating the global point cloud data;
s4, keeping the measured object still, changing a measurement view angle to measure the measured object, and repeating the steps S2 to S3 until the measured object is measured;
and S5, performing global optimization processing on the global point cloud data updated after the measurement is completed to obtain a point cloud model.
2. The three-dimensional reconstruction method according to claim 1, wherein the step S2 specifically comprises the steps of:
s21, performing three-dimensional measurement on the surface of the local area to obtain a first speckle image and a second speckle image of the local area;
s22, determining a whole pixel level corresponding point of each pixel in the first speckle image in the second speckle image;
step S23, searching sub-pixel corresponding points of the second speckle image according to the whole pixel level corresponding points and the coordinates of all pixel points in the first speckle image to obtain sub-pixel corresponding points in the second speckle image;
and S24, performing three-dimensional reconstruction by combining the binocular stereoscopic vision principle and the corresponding relation of the sub-pixels of the second speckle image to obtain local point cloud data of the surface of the measured object.
3. The three-dimensional reconstruction method according to claim 2, wherein the step S22 specifically includes the steps of:
step S221, the first speckle image sum is calculated according to a correlation calculation formulaThe second speckle image is selected to be the same size (2)w m +1)×(2w m + 1) pixel area, and the correlation calculation formula is:
wherein the content of the first and second substances,
representing the gray value of a first speckle image plane point (u, v) in the selected area, and/or>
Represents the gray value of a second speckle image plane point (u, v) in the selected area, and/or>
And &>
Represents the mean value of the gray levels of the selected areas of the first speckle image and the second speckle image, respectively, and->
The correlation coefficient is represented by a correlation coefficient,w m representing a number of pixels;
step S222, selecting a corresponding point corresponding to the maximum value of the calculated value of the correlation coefficient and exceeding the set threshold as a corresponding point at the whole pixel level.
4. The three-dimensional reconstruction method according to claim 1, wherein the optimizing the global point cloud data in step S5 comprises: and establishing an error evaluation function uniformly containing all the overlapping regions, and averagely distributing the registration errors to all the overlapping regions through the solution of the error evaluation function so as to reduce the accumulation of the registration errors.
5. A three-dimensional reconstruction system, comprising:
a three-dimensional measurement module to: calibrating the three-dimensional reconstruction coordinate system; acquiring point cloud data of a first frame of three-dimensional measurement data of a measured object, wherein the point cloud data is called global point cloud data, and a coordinate system based on the first frame of three-dimensional measurement data is called a global coordinate system;
the system is also used for carrying out three-dimensional measurement on the surface of a certain local area of a measured object, carrying out three-dimensional reconstruction by utilizing a binocular stereo vision principle to obtain point cloud data of the local area, wherein an overlapping area exists between the local area and an area corresponding to the global point cloud data;
the global point cloud data updating module is used for transforming the local point cloud data to the global coordinate system, registering the local point cloud data and the global point cloud data according to the overlapping area and updating the global point cloud data;
and the global point cloud data optimization module is used for carrying out global optimization processing on the global point cloud data obtained after the measurement is finished to obtain a point cloud model.
6. The three-dimensional reconstruction system of claim 5 wherein said three-dimensional reconstruction system is piggybacked on a drone platform for three-dimensional measurements.
7. A mobile terminal, comprising: a processor, a memory communicatively connected to the processor, the memory storing a computer program, the processor being configured to invoke the computer program in the memory to implement the three-dimensional reconstruction method according to any one of claims 1 to 4.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that can be executed to implement the three-dimensional reconstruction method according to any one of claims 1 to 4.
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