CN117726518A - Three-dimensional point cloud data coordinate conversion device and method based on deep learning - Google Patents
Three-dimensional point cloud data coordinate conversion device and method based on deep learning Download PDFInfo
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Abstract
The invention discloses a three-dimensional point cloud data coordinate conversion device and a three-dimensional point cloud data coordinate conversion method based on deep learning, wherein the coordinate conversion device comprises a conversion host, a display screen is arranged on the conversion host, an operation platform is arranged below the display screen, a data transmission interface is arranged on one side of the conversion host, a controller is arranged at the bottom of the conversion host, a central processing unit, a three-dimensional point cloud data acquisition module, a three-dimensional point cloud data model import module, a training data set generation module, a three-dimensional point cloud data segmentation module, a data characteristic extraction module and a data coordinate conversion module are arranged in the controller, the three-dimensional point cloud data acquisition module is connected with the three-dimensional point cloud data model import module, the three-dimensional point cloud data model import module is connected with the central processing unit, the training data set generation module, the three-dimensional point cloud data segmentation module, the data characteristic extraction module and the data coordinate conversion module are respectively connected with the central processing unit.
Description
Technical Field
The invention relates to the technical field of data coordinate conversion, in particular to a three-dimensional point cloud data coordinate conversion device and method based on deep learning.
Background
Point cloud data refers to a set of vectors in a three-dimensional coordinate system. In addition to having geometric positions, the point cloud data has color information. The color information is typically obtained by capturing a color image with a camera, and then assigning color information of pixels at corresponding positions to corresponding points in a point cloud. The intensity information is obtained by the echo intensity collected by the receiving device of the laser scanner, and the intensity information is related to the surface material, roughness, incident angle direction of the target and the emission energy of the instrument, and the laser wavelength.
Currently, coordinate transformation has been related to applications of various industries, such as car navigation positioning, resource investigation, remote sensing image analysis, city construction and management, and the like. In the prior art, three-dimensional point cloud data coordinate conversion generally adopts the coordinate conversion technology of obj, b3dm and other formats, and has low conversion efficiency, so that improvement is needed.
Disclosure of Invention
The invention aims to provide a three-dimensional point cloud data coordinate conversion device and method based on deep learning, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the three-dimensional point cloud data coordinate conversion device based on deep learning comprises a conversion host, a display screen is installed on the conversion host, an operation platform is arranged below the display screen, a data transmission interface is arranged on one side of the conversion host, a controller is installed at the bottom of the conversion host, and a central processing unit, a three-dimensional point cloud data acquisition module, a three-dimensional point cloud data model importing module, a training data set generating module, a three-dimensional point cloud data segmentation module, a data feature extraction module and a data coordinate conversion module are installed in the controller.
Preferably, the three-dimensional point cloud data coordinate conversion device based on deep learning, provided by the application, wherein the three-dimensional point cloud data acquisition module is connected with the three-dimensional point cloud data model import module, the three-dimensional point cloud data model import module is connected with the central processing unit, and the training data set generation module, the three-dimensional point cloud data segmentation module, the data feature extraction module and the data coordinate conversion module are respectively connected with the central processing unit; the three-dimensional point cloud data acquisition module is used for acquiring three-dimensional coordinates of point cloud data; the three-dimensional point cloud data model importing module is used for importing a three-dimensional data model; the training data set generation module is used for generating an countermeasure network CGAN; the three-dimensional point cloud data segmentation module is used for segmenting three-dimensional data; the data characteristic extraction module can accurately extract characteristic signals of the point cloud data; the data coordinate conversion module is used for carrying out coordinate conversion on the three-dimensional point cloud data after the feature extraction.
Preferably, the three-dimensional point cloud data coordinate conversion device based on deep learning provided by the application, wherein the training data set generating module generates a training image data set, the training image data set is divided into a training set, a verification set and a test set, the training set firstly trains the condition generation countermeasure network CGAN, the verification set verifies the condition generation countermeasure network CGAN, the test set tests the condition generation countermeasure network CGAN, and finally the trained condition generation countermeasure network CGAN is obtained.
Preferably, the application provides a three-dimensional point cloud data coordinate conversion device based on deep learning, wherein the application method thereof comprises the following steps:
A. firstly, a three-dimensional point cloud data acquisition module acquires three-dimensional point cloud data;
B. the acquired three-dimensional point cloud data is imported into a three-dimensional point cloud data model importing module to generate a three-dimensional data model;
C. the three-dimensional data model is input into a training data set generation module for deep learning, and is used for generating an antagonism network CGAN;
D. transmitting the three-dimensional data model subjected to deep learning to a three-dimensional point cloud data segmentation module for data segmentation;
E. the segmented data are transmitted to a data feature extraction module for data feature extraction;
F. and finally, transmitting the data with the extracted data characteristics to a data coordinate conversion module for coordinate conversion.
Preferably, the three-dimensional point cloud data coordinate conversion device based on deep learning provided by the application, wherein the three-dimensional point cloud data segmentation module segments the following method:
a. the method comprises the steps of carrying out partial segmentation on three-dimensional point cloud data to be processed, extracting, storing the extracted data in a separate library, and avoiding the situation that data are damaged and lost due to mixing and messy codes between the data in the data segmentation process;
b. the extracted data is subjected to safety detection, the messy codes and the mixed codes in the data are removed, and the data is subjected to comprehensive safety detection, so that the condition that the virus data causes damage to a system is avoided;
c. constructing a secondary feature matrix of the data subjected to risk assessment so as to divide the data, facilitate the arrangement of the data and improve the data processing efficiency;
d. and carrying out segmentation processing on the data which is subjected to secondary feature matrix construction, segmenting the data into a plurality of sub-data files, and automatically generating the segmentation state of each sub-data file.
Preferably, the three-dimensional point cloud data coordinate conversion device based on deep learning provided by the application, wherein the data feature extraction module extraction method is as follows:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
Preferably, the three-dimensional point cloud data coordinate conversion device based on deep learning provided by the application, wherein the data coordinate conversion module conversion method is as follows:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ x ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ x ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system.
Compared with the prior art, the invention has the beneficial effects that:
(1) The three-dimensional point cloud data acquisition method is high in intelligent degree, capable of carrying out three-dimensional point cloud data acquisition, deep learning, data segmentation, feature extraction and coordinate conversion, high in conversion accuracy and capable of improving three-dimensional data processing efficiency.
(2) The three-dimensional point cloud data segmentation module segmentation method can segment huge data, and effectively avoid mixing of messy codes, mixed codes and virus data and improve data segmentation safety through data risk evaluation before data segmentation.
(3) According to the point cloud data feature extraction module extraction method, the first keywords and the second keywords are searched, so that the extraction difficulty can be reduced, and the feature extraction precision is improved.
(4) The data coordinate conversion module conversion method adopted by the invention has high data conversion efficiency, can automatically select conversion parameters of original three-dimensional data, and improves conversion efficiency and accuracy.
Drawings
FIG. 1 is a schematic diagram of the structure of the present invention;
FIG. 2 is a schematic block diagram of an architecture of the present invention;
FIG. 3 is a flow chart of the operation of the present invention;
FIG. 4 is a flow chart of a three-dimensional point cloud data segmentation module according to the present invention;
FIG. 5 is a flow chart of a point cloud data feature extraction module according to the present invention;
in the figure: the three-dimensional point cloud data processing system comprises a conversion host 1, a display screen 2, an operation platform 3, a data transmission interface 4, a controller 5, a central processing unit 6, a three-dimensional point cloud data acquisition module 7, a three-dimensional point cloud data model importing module 8, a training data set generating module 9, a three-dimensional point cloud data segmentation module 10, a data feature extraction module 11 and a data coordinate conversion module 12.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-5, the present invention provides a technical solution: the three-dimensional point cloud data coordinate conversion device based on deep learning comprises a conversion host 1, wherein a display screen 2 is installed on the conversion host 1, an operation platform 3 is arranged below the display screen 2, a data transmission interface 4 is arranged on one side of the conversion host 1, a controller 5 is installed at the bottom of the conversion host 1, and a central processing unit 6, a three-dimensional point cloud data acquisition module 7, a three-dimensional point cloud data model import module 8, a training data set generation module 9, a three-dimensional point cloud data segmentation module 10, a data feature extraction module 11 and a data coordinate conversion module 12 are installed in the controller 5.
In the invention, a three-dimensional point cloud data acquisition module 7 is connected with a three-dimensional point cloud data model import module 8, the three-dimensional point cloud data model import module 8 is connected with a central processor 6, and a training data set generation module 9, a three-dimensional point cloud data segmentation module 10, a data characteristic extraction module 11 and a data coordinate conversion module 12 are respectively connected with the central processor 6; the three-dimensional point cloud data acquisition module is used for acquiring three-dimensional coordinates of point cloud data; the three-dimensional point cloud data model importing module is used for importing a three-dimensional data model; the training data set generation module is used for generating an countermeasure network CGAN; the three-dimensional point cloud data segmentation module is used for segmenting three-dimensional data; the data characteristic extraction module can accurately extract characteristic signals of the point cloud data; the data coordinate conversion module is used for carrying out coordinate conversion on the three-dimensional point cloud data after the feature extraction.
The training data set generation module generates a training image data set, the training image data set is divided into a training set, a verification set and a test set, the training set firstly trains the condition generation countermeasure network CGAN, then verifies the condition generation countermeasure network CGAN through the verification set, finally tests the condition generation countermeasure network CGAN through the test set, and finally obtains the trained condition generation countermeasure network CGAN.
Working principle: the application method of the invention comprises the following steps:
A. firstly, a three-dimensional point cloud data acquisition module acquires three-dimensional point cloud data;
B. the acquired three-dimensional point cloud data is imported into a three-dimensional point cloud data model importing module to generate a three-dimensional data model;
C. the three-dimensional data model is input into a training data set generation module for deep learning, and is used for generating an antagonism network CGAN;
D. transmitting the three-dimensional data model subjected to deep learning to a three-dimensional point cloud data segmentation module for data segmentation;
E. the segmented data are transmitted to a data feature extraction module for data feature extraction;
F. and finally, transmitting the data with the extracted data characteristics to a data coordinate conversion module for coordinate conversion.
In the invention, the three-dimensional point cloud data segmentation module segmentation method comprises the following steps:
a. the method comprises the steps of carrying out partial segmentation on three-dimensional point cloud data to be processed, extracting, storing the extracted data in a separate library, and avoiding the situation that data are damaged and lost due to mixing and messy codes between the data in the data segmentation process;
b. the extracted data is subjected to safety detection, the messy codes and the mixed codes in the data are removed, and the data is subjected to comprehensive safety detection, so that the condition that the virus data causes damage to a system is avoided;
c. constructing a secondary feature matrix of the data subjected to risk assessment so as to divide the data, facilitate the arrangement of the data and improve the data processing efficiency;
d. and carrying out segmentation processing on the data which is subjected to secondary feature matrix construction, segmenting the data into a plurality of sub-data files, and automatically generating the segmentation state of each sub-data file.
The three-dimensional point cloud data segmentation module segmentation method can segment huge data, and effectively avoid mixing of messy codes, mixed codes and virus data and improve data segmentation safety through data risk evaluation before data segmentation.
In the invention, the extraction method of the data characteristic extraction module comprises the following steps:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
According to the point cloud data feature extraction module extraction method, the first keywords and the second keywords are searched, so that the extraction difficulty can be reduced, and the feature extraction precision is improved.
In addition, in the invention, the data coordinate conversion module conversion method is as follows:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ x ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ x ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system. The data coordinate conversion module conversion method adopted by the invention has high data conversion efficiency, can automatically select conversion parameters of original three-dimensional data, and improves conversion efficiency and accuracy.
In conclusion, the three-dimensional point cloud data processing method has high intelligent degree, can be used for three-dimensional point cloud data acquisition, deep learning, data segmentation, feature extraction and coordinate conversion, has high conversion accuracy, and improves three-dimensional data processing efficiency.
The circuit, the electronic components and the modules are all in the prior art, and can be completely realized by a person skilled in the art, and needless to say, the protection of the invention does not relate to the improvement of software and a method.
Standard parts used in the application files can be purchased from the market, and can be customized according to the description of the specification and the drawing, the specific connection modes of the parts are conventional means such as mature bolts, rivets and welding in the prior art, and the machines, the parts and the equipment are of conventional types in the prior art.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. Three-dimensional point cloud data coordinate conversion device based on degree of depth study, coordinate conversion device includes conversion host computer (1), its characterized in that: the device comprises a conversion host (1), wherein a display screen (2) is installed on the conversion host (1), an operation platform (3) is arranged below the display screen (2), a data transmission interface (4) is arranged on one side of the conversion host (1), a controller (5) is installed at the bottom of the conversion host (1), a central processing unit (6), a three-dimensional point cloud data acquisition module (7), a three-dimensional point cloud data model importing module (8), a training data set generating module (9), a three-dimensional point cloud data segmentation module (10), a data feature extraction module (11) and a data coordinate conversion module (12) are installed in the controller (5).
2. The deep learning-based three-dimensional point cloud data coordinate conversion device according to claim 1, wherein: the three-dimensional point cloud data acquisition module (7) is connected with the three-dimensional point cloud data model import module (8), the three-dimensional point cloud data model import module (8) is connected with the central processing unit (6), and the training data set generation module (9), the three-dimensional point cloud data segmentation module (10), the data characteristic extraction module (11) and the data coordinate conversion module (12) are respectively connected with the central processing unit (6); the three-dimensional point cloud data acquisition module is used for acquiring three-dimensional coordinates of point cloud data; the three-dimensional point cloud data model importing module is used for importing a three-dimensional data model; the training data set generation module is used for generating an countermeasure network CGAN; the three-dimensional point cloud data segmentation module is used for segmenting three-dimensional data; the data characteristic extraction module can accurately extract characteristic signals of the point cloud data; the data coordinate conversion module is used for carrying out coordinate conversion on the three-dimensional point cloud data after the feature extraction.
3. The deep learning-based three-dimensional point cloud data coordinate conversion device according to claim 1, wherein: the training data set generation module generates a training image data set, the training image data set is divided into a training set, a verification set and a test set, the training set firstly trains the condition generation countermeasure network CGAN, then verifies the condition generation countermeasure network CGAN through the verification set, finally tests the condition generation countermeasure network CGAN through the test set, and finally obtains the trained condition generation countermeasure network CGAN.
4. The application method of the three-dimensional point cloud data coordinate conversion device based on the deep learning is realized, which is characterized in that: the application method comprises the following steps:
A. firstly, a three-dimensional point cloud data acquisition module acquires three-dimensional point cloud data;
B. the acquired three-dimensional point cloud data is imported into a three-dimensional point cloud data model importing module to generate a three-dimensional data model;
C. the three-dimensional data model is input into a training data set generation module for deep learning, and is used for generating an antagonism network CGAN;
D. transmitting the three-dimensional data model subjected to deep learning to a three-dimensional point cloud data segmentation module for data segmentation;
E. the segmented data are transmitted to a data feature extraction module for data feature extraction;
F. and finally, transmitting the data with the extracted data characteristics to a data coordinate conversion module for coordinate conversion.
5. The method for using the three-dimensional point cloud data coordinate conversion device based on deep learning according to claim 4, wherein the method comprises the following steps: the three-dimensional point cloud data segmentation module segmentation method comprises the following steps:
a. the method comprises the steps of carrying out partial segmentation on three-dimensional point cloud data to be processed, extracting, storing the extracted data in a separate library, and avoiding the situation that data are damaged and lost due to mixing and messy codes between the data in the data segmentation process;
b. the extracted data is subjected to safety detection, the messy codes and the mixed codes in the data are removed, and the data is subjected to comprehensive safety detection, so that the condition that the virus data causes damage to a system is avoided;
c. constructing a secondary feature matrix of the data subjected to risk assessment so as to divide the data, facilitate the arrangement of the data and improve the data processing efficiency;
d. and carrying out segmentation processing on the data which is subjected to secondary feature matrix construction, segmenting the data into a plurality of sub-data files, and automatically generating the segmentation state of each sub-data file.
6. The method for using the three-dimensional point cloud data coordinate conversion device based on deep learning according to claim 4, wherein the method comprises the following steps: the data feature extraction module comprises the following steps:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
7. The method for using the three-dimensional point cloud data coordinate conversion device based on deep learning according to claim 4, wherein the method comprises the following steps: the data coordinate conversion module conversion method comprises the following steps:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ x ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ x ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system.
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