Method, device and equipment for reconstructing assembled grain three-dimensional model

Abstract

The invention discloses a method, device and equipment for reconstructing an assembled grain three-dimensional model, and relates to the technical field of three-dimensional model construction.According to the method, end face measurement data is used for constructing a grain digital model, and aiming at the problems that an existing point cloud reconstruction technology depends on large-space detection (shooting) equipment and is lack of reconstruction model precision, an end face shooting mode is adopted, and the reconstruction precision is improved. And integrating optimized data into new point cloud data by using a radius outlier removal (ROR) algorithm, a convex hull algorithm and a least square method according to the obtained point cloud initial data, and finally reconstructing a high-precision three-dimensional geometric model. According to the method, the precision of the point cloud reconstruction model is improved, and the applicable scene is expanded.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional model construction, in particular to a method, device and equipment for reconstructing a three-dimensional model of an assembled propellant column by using end face measurement data. BACKGROUND

[0002] The three-dimensional modeling method often depends on an idealized design model, but in the actual production process, the idealized model and the actual assembled body geometric size have certain deviations due to the restriction of processing technology and equipment precision. In order to meet the precise requirements of the subsequent assembly process on the geometric structure of the assembled body, it is necessary to accurately reconstruct the real geometric structure.

[0003] Point cloud data opens up a new way for accurate modeling due to its authenticity and real-time nature. However, point cloud data often contains a large amount of noise, which poses a challenge to the accuracy of the model and the subsequent boundary extraction and end face construction.

[0004] In the prior art, the point cloud reconstruction technology mainly adopts the method of establishing a motion mechanism or a multi-point measurement device to obtain multi-angle or multi-vertex shooting image data, realizes the acquisition of point cloud data, and then calculates the spatial coordinates of the point pixels through spatial geometry method, and then performs mathematical fitting to reconstruct a three-dimensional geometric model.

[0005] However, the multi-angle and multi-point measurement method is too dependent on the detection equipment, and it is difficult to implement in some environments with limited space range or in the case of adding to the existing device. At the same time, the existing three-dimensional reconstruction technology lacks further noise reduction processing after acquiring point cloud data, and the accuracy of the reconstructed model is not further improved through the screening and research of optimization algorithms. For some scenes with high accuracy requirements for geometric models, such as high-precision assembly, it is difficult to meet the reconstruction requirements.

[0006] Therefore, how to provide a method capable of efficiently reducing noise, accurately extracting boundaries and completing surface construction to improve the accuracy of the model is an urgent technical problem for those skilled in the art to solve. SUMMARY

[0007] In view of the above problems, the present application provides a method, device and equipment for reconstructing a three-dimensional model of an assembled propellant column to overcome the above problems or at least partially solve the above problems.

[0008] The present application provides the following solutions:

[0009] A method for reconstructing a three-dimensional model of an assembled propellant column, comprising:

[0010] acquiring three-dimensional initial point cloud data of the propellant column generated by using an end face shooting method and MATLAB software;

[0011] determine a target denoising method according to the characteristics and noise type of the initial point cloud data, and use the target denoising method to perform denoising processing on the initial point cloud data to obtain denoised point cloud data;

[0012] project the denoised point cloud data onto a two-dimensional plane, determine a target boundary extraction method according to the type and distribution characteristics of the denoised point cloud data, and use the target boundary extraction method to screen out a plurality of target boundary points on the projection plane;

[0013] fit a plurality of the target boundary points to obtain a boundary point fitting curve;

[0014] compare the point cloud on the initial point cloud data that is not on the fitting curve with the boundary point cloud on the fitting curve, find out the points that are different from the boundary point cloud on the fitting curve as transition point cloud, and combine the transition point cloud with the boundary point cloud on the fitting curve to obtain new point cloud data;

[0015] use a three-dimensional entity modeling software to import the new point cloud data, perform cylindrical construction, and generate a three-dimensional model of the drug column.

[0016] Preferably, the target denoising method includes any one of a radius outlier removal algorithm, a statistical outlier removal algorithm, a bilateral filtering method, and a voxel grid filtering method.

[0017] Preferably, the target denoising method includes a radius outlier removal algorithm.

[0018] Preferably, the point cloud data is projected onto a two-dimensional plane using an orthogonal projection method.

[0019] Preferably, the target boundary extraction method includes any one of a convex hull algorithm and an Alpha shape algorithm.

[0020] Preferably, the target boundary extraction method includes a convex hull algorithm to find the boundary points of the smallest convex polygon or convex polyhedron that encloses all point cloud data as the approximate boundary of the zero drug column.

[0021] Preferably, a plurality of the target boundary points are fitted to a boundary point fitting curve using a least squares method.

[0022] Preferably, when performing digital model construction using a three-dimensional entity modeling software, any one of a mesh surface reconstruction method, an implicit surface reconstruction method, and a parameter surface reconstruction method is used.

[0023] A device for reconstructing and assembling a three-dimensional model of a drug column is used to perform the above-mentioned method for reconstructing and assembling a three-dimensional model of a drug column, and the device includes:

[0024] An initial point cloud acquisition unit is configured to acquire three-dimensional initial point cloud data of a propellant grain generated by using MATLAB software in an end face shooting mode.

[0025] A de-noising unit is configured to determine a target de-noising method according to the characteristics and noise types of the initial point cloud data, and to perform de-noising processing on the initial point cloud data by using the target de-noising method to obtain de-noised point cloud data.

[0026] A projection and boundary extraction unit is configured to project the de-noised point cloud data onto a two-dimensional plane, to determine a target boundary extraction method according to the types and distribution characteristics of the de-noised point cloud data, and to screen out a plurality of target boundary points on the projection plane by using the target boundary extraction method.

[0027] A curve fitting unit is configured to perform curve fitting on the plurality of target boundary points to obtain a boundary point fitting curve.

[0028] A new point cloud data generation unit is configured to compare the point cloud on the initial point cloud data that is not on the fitting curve with the boundary point cloud on the fitting curve, to find out points that are different from the boundary point cloud on the fitting curve as transition point cloud, and to combine the transition point cloud with the boundary point cloud on the fitting curve to obtain new point cloud data.

[0029] A three-dimensional model construction unit is configured to use three-dimensional entity modeling software, to import the new point cloud data, to perform cylinder construction, and to generate a three-dimensional model of the propellant grain.

[0030] A device for reconstructing and assembling a three-dimensional model of a propellant grain includes a processor and a memory.

[0031] The memory is configured to store program code and transmit the program code to the processor.

[0032] The processor is configured to execute the above-mentioned method for reconstructing and assembling a three-dimensional model of a propellant grain according to instructions in the program code.

[0033] According to the specific embodiments of the present application, the following technical effects are achieved.

[0034] The method provided by the embodiments of the present application uses end face measurement data to construct a propellant grain numerical model, and solves the problems of the current point cloud reconstruction technology, such as dependence on large space detection (shooting) equipment and lack of reconstruction model precision. The method uses an end face shooting mode to acquire initial point cloud data, uses a radius outlier removal algorithm (ROR), a convex hull algorithm and a least square method to integrate the optimized data into new point cloud data, and finally reconstructs a high-precision three-dimensional geometric model. The method improves the point cloud reconstruction model precision and also expands the applicable scenarios.

[0035] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0037] Figure 1 This is a flow chart of a method for reconstructing a three-dimensional model of an assembled grain provided by an embodiment of the present invention;

[0038] Figure 2 is a flow chart of a simulation point cloud algorithm provided by an embodiment of the present invention;

[0039] Figure 3 This is a simulated point cloud data graph provided by an embodiment of the present invention;

[0040] Figure 4 is a flow chart of the ROR algorithm provided by an embodiment of the present invention;

[0041] Figure 5 This is a ROR denoising analysis diagram provided by an embodiment of the present invention;

[0042] Figure 6 is a flow chart of the SOR noise removal algorithm provided by an embodiment of the present invention;

[0043] Figure 7 This is a diagram showing the SOR noise removal effect provided by an embodiment of the present invention;

[0044] Figure 8 is a flow chart of a bilateral filtering algorithm provided by an embodiment of the present invention;

[0045] Figure 9 This is a bilateral filtering noise reduction analysis diagram provided by an embodiment of the present invention;

[0046] Figure 10 is a flow chart of voxel grid filtering and denoising provided by an embodiment of the present invention;

[0047] Figure 11 This is a diagram showing the effect of voxel filtering and denoising provided by an embodiment of the present invention;

[0048] Figure 12 This is a flowchart of point cloud denoising provided by an embodiment of the present invention;

[0049] Figure 13 This is a point cloud projection flow chart provided by an embodiment of the present invention;

[0050] Figure 14 is a point cloud projection diagram provided by an embodiment of the present application;

[0051] Figure 15 is a convex hull algorithm flowchart provided by an embodiment of the present application;

[0052] Figure 16 is a convex hull algorithm boundary extraction point schematic diagram provided by an embodiment of the present application;

[0053] Figure 17 is an Alpha shape algorithm principle diagram provided by an embodiment of the present application;

[0054] Figure 18 is an Alpha shape contour point judgment schematic diagram provided by an embodiment of the present application;

[0055] Figure 19 is an Alpha shape algorithm flowchart provided by an embodiment of the present application;

[0056] Figure 20 is an Alpha shape algorithm boundary extraction point schematic diagram provided by an embodiment of the present application;

[0057] Figure 21 is a least square method curve fitting flowchart provided by an embodiment of the present application;

[0058] Figure 22 is a curve fitting boundary point cloud provided by an embodiment of the present application;

[0059] Figure 23 is a combination construction point cloud flowchart provided by an embodiment of the present application;

[0060] Figure 24 is a combination construction new point cloud diagram provided by an embodiment of the present application;

[0061] Figure 25 is a three-dimensional model diagram of a part provided by an embodiment of the present application;

[0062] Figure 26 is a schematic diagram of a device for reconstructing and assembling a three-dimensional model of a medicine column provided by an embodiment of the present application;

[0063] Figure 27 is a schematic diagram of a device for reconstructing and assembling a three-dimensional model of a medicine column provided by an embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0065] Referring to Figure 1 A method for reconstructing and assembling a three-dimensional model of a drug column is provided in the embodiments of the present application, as shown in the figure, the method can comprise: Figure 1

[0066] S101: Obtain three-dimensional initial point cloud data of a drug column generated by MATLAB software using an end face shooting method;

[0067] S102: Determine a target denoising method according to the characteristics and noise types of the initial point cloud data, and perform denoising processing on the initial point cloud data by using the target denoising method to obtain denoised point cloud data; in specific implementation, the present application embodiment can provide that the target denoising method comprises any one of a radius outlier removal algorithm, a statistical outlier removal algorithm, a bilateral filtering method and a voxel grid filtering method. Further, the target denoising method comprises a radius outlier removal algorithm.

[0068] S103: Project the denoised point cloud data onto a two-dimensional plane, determine a target boundary extraction method according to the type and distribution characteristics of the denoised point cloud data, and use the target boundary extraction method to screen out a plurality of target boundary points on the projection plane; in specific implementation, the present application embodiment can provide that the point cloud data is projected onto a two-dimensional plane by using an orthogonal projection method.

[0069] The target boundary extraction method comprises any one of a convex hull algorithm and an Alpha shape algorithm. Further, the target boundary extraction method comprises a convex hull algorithm, so as to find the boundary points of the smallest convex polygon or convex polyhedron that encloses all point cloud data, as the approximate boundary of the zero drug column.

[0070] S104: Curve fitting is performed on a plurality of target boundary points to obtain a boundary point fitting curve; in specific implementation, the present application embodiment can provide that a plurality of target boundary points are sampled and curve fitted by using a least square method to obtain a boundary point fitting curve.

[0071] S105: Compare the point cloud on the initial point cloud data that is not on the fitting curve with the boundary point cloud on the fitting curve, find out the points that are different from the boundary point cloud on the fitting curve as transition point cloud, and combine the transition point cloud with the boundary point cloud on the fitting curve to obtain new point cloud data;

[0072] ​S106: using a three-dimensional entity modeling software, importing the new point cloud data, performing cylindrical surface construction, and generating a three-dimensional model of the drug column. When constructing the numerical model using the three-dimensional entity modeling software, any one of the grid surface reconstruction method, the implicit surface reconstruction method, and the parameter surface reconstruction method is adopted.

[0073] The embodiment of the present application provides a three-dimensional model reconstruction method of a drug column, which aims at the problems of the current point cloud reconstruction technology, such as dependence on large space detection (shooting) equipment and lack of model reconstruction accuracy. The method adopts an end face shooting mode to obtain initial point cloud data, uses a radius outlier removal algorithm (ROR), a convex hull algorithm, and a least square method to integrate the data after optimization into new point cloud data, and finally reconstructs a high-precision three-dimensional geometric model. The method improves the point cloud reconstruction model accuracy, expands the applicable scenarios, and enhances the flexibility of the numerical model construction mode.

[0074] The method provided by the present application will be described in detail below.

[0075] The present application provides a three-dimensional model reconstruction method of a drug column, which uses point cloud data to construct an end face numerical model of the drug column. The method comprises the following steps:

[0076] 1. Point cloud data acquisition: the point cloud data is acquired by an end face shooting mode, and the point cloud data is acquired by MATLAB software.

[0077] The point cloud data is acquired according to Figure 2 The simulation point cloud data graph shown in Figure 3

[0078] 2. Point cloud data denoising: the acquired point cloud data is preprocessed, including removing noise and outliers. A statistical outlier removal algorithm (SOR) is used to denoise the point cloud data, so as to improve the quality of the point cloud data and the accuracy of subsequent processing.

[0079] S21, selecting a denoising method: according to the characteristics of the point cloud data and the type of noise, a suitable denoising method is selected. In the present application, a radius outlier removal algorithm (ROR), a statistical outlier removal algorithm (SOR), bilateral filtering, and voxel grid filtering are tried, and their denoising effects are compared.

[0080] S22, performing a denoising operation: for the ROR method, a suitable radius and threshold are set, and the outlier points are identified and removed. The flow and effect are shown in Figure 4 and Figure 5

[0081] For the SOR method, the distance of each point to its neighborhood points is calculated, and the outlier points are judged and removed according to the average value and standard deviation of the distance. The flow and effect are shown in​​Figure 6 and Figure 7 as shown.

[0082] For the bilateral filtering method, the weight is calculated according to the Euclidean distance and the pixel value difference between pixels, and the weighted average filtering is performed, and the flow and effect are as shown in Figure 8 and Figure 9 as shown.

[0083] For the voxel grid filtering method, the point cloud data is divided into small three-dimensional cubic units, and the aggregation operation is performed on the points inside each voxel, and the flow and effect are as shown in Figure 10 and Figure 11 as shown.

[0084] S23, compare the point cloud data processed by different denoising methods, and evaluate the denoising effect. According to the actual application requirements, the best denoising method, radius outlier removal algorithm (ROR), is selected, and the flowchart of the four methods is as shown in Figure 12 When the statistical outlier removal algorithm (SOR) is used to denoise the point cloud data, the algorithm parameters are flexibly adjusted according to the characteristics and noise level of the point cloud data to achieve the best effect.

[0085] 3, point cloud data boundary extraction, the preprocessed point cloud data is projected onto the selected projection plane. The orthogonal projection method is selected to project the point cloud data onto the xoy plane to obtain the grain column contour information. Considering the data structure and characteristics of the point cloud, as well as the requirements of subsequent processing, it is ensured that the two-dimensional contour information after projection can accurately reflect the grain column contour. The boundary extraction is performed on the projected point cloud data. The convex hull algorithm is used to extract the boundary points of the point cloud data as the basis for subsequent curve fitting.

[0086] S31, project the point cloud data: project the three-dimensional point cloud data onto a two-dimensional plane to facilitate subsequent processing, and the flow and effect are as shown in Figure 13 and Figure 14 as shown.

[0087] S32, select the boundary extraction method: according to the type and distribution characteristics of the point cloud data, select the appropriate boundary extraction method. In this invention, the convex hull algorithm and the Alpha shape algorithm are tried, and the flow and effect are as shown in Figures 15 to 20 as shown.

[0088] S33, implement the boundary extraction operation: for the convex hull algorithm, traverse the point cloud data, find the outermost points, perform polar angle sorting, and construct the convex hull.

[0089] For the Alpha shape algorithm, set a suitable rolling radius alpha, judge the boundary points, and iteratively obtain the boundary point set.

[0090] S34, Evaluate the boundary extraction effect: compare the results processed by different boundary extraction methods to evaluate the boundary extraction effect. According to the actual application requirements, select the best boundary extraction method - convex hull algorithm. When extracting the boundary points of point cloud data using the convex hull algorithm, traverse the point cloud data to find the boundary points of the smallest convex polygon (in two-dimensional case) or convex polyhedron (in three-dimensional case) that can surround all point cloud data, as the approximate boundary of the zero propellant column.

[0091] 4, Point cloud curve fitting and surface reconstruction, least squares method is used for curve fitting to ensure that the final fitted boundary is the most ideal result of the propellant column boundary. Replace the boundary points of the original point cloud data with the extracted boundary points to generate new point cloud data to more comprehensively reflect the three-dimensional shape of the propellant column. Use three-dimensional modeling software to construct the end face of the propellant column using the new point cloud data to generate a three-dimensional model of the propellant column, which can be used for subsequent manufacturing and processing.

[0092] S41, Point cloud curve fitting: according to the extracted boundary points, curve fitting is performed. Least squares method is used to fit the boundary point curve, and the process and effect are shown in Figure 21 and Figure 22 .

[0093] S42, Combine to build new point cloud: compare the points on the original point cloud that are not on the fitted curve with the boundary point cloud of the fitted curve, find the differences, and combine them into new point cloud data, and the process and effect are shown in Figure 23 and Figure 24 .

[0094] S43, Surface reconstruction: import the new point cloud data into the three-dimensional solid modeling software and perform cylindrical surface construction. Through the surface fitting tool, a three-dimensional model is generated, and the final result is shown in Figure 25 . When using three-dimensional design software to construct the numerical model of the new point cloud data, you can choose grid surface reconstruction, implicit surface reconstruction or parameter surface reconstruction according to actual requirements to generate a three-dimensional model that meets the design specifications.

[0095] S44, Evaluate the reconstructed three-dimensional model to check its accuracy and integrity. According to the evaluation results, optimize and adjust the model to meet the actual application requirements. This part has accurate three-dimensional shape and size information, meets the design specifications, and can be used for mechanical manufacturing, engineering design or other related fields.

[0096] The existing point cloud reconstruction technology mainly adopts the method of establishing a motion mechanism or a multi-point measurement device to obtain multi-angle or multi-vertex shooting image data, which is difficult to realize in some environments with limited space or in the case of adding to the existing device. At the same time, the existing three-dimensional reconstruction technology lacks further noise reduction processing after obtaining the point cloud data, and the accuracy of the reconstructed model is not improved through the screening and research of optimization algorithms, which is difficult to meet the reconstruction requirements for some scenes with high accuracy requirements for geometric models, such as high-precision assembly.

[0097] Compared with the prior art, the application provides a method for reconstructing a three-dimensional model of an assembled drug column, which uses end face measurement data to construct a drug column numerical model. In view of the problems of the existing point cloud reconstruction technology, such as dependence on large space detection (shooting) equipment and lack of reconstruction model accuracy, an end face shooting method is adopted to obtain initial point cloud data. The data after optimization processing is integrated into new point cloud data by using the radius outlier removal algorithm (ROR), the convex hull algorithm and the least squares method, and finally a high-precision three-dimensional geometric model is reconstructed. This method improves the accuracy of the point cloud reconstruction model and also expands the applicable scenarios.

[0098] Referring to Figure 26 , the embodiment of the application can also provide a device for reconstructing a three-dimensional model of an assembled drug column, as shown in Figure 26 , which is used to execute the above-mentioned method for reconstructing a three-dimensional model of an assembled drug column. The device can include:

[0099] An initial point cloud acquisition unit 2601 is configured to acquire three-dimensional initial point cloud data of a drug column generated by using MATLAB software in an end face shooting mode;

[0100] A denoising unit 2602 is configured to determine a target denoising method according to the characteristics and noise types of the initial point cloud data, and to perform denoising processing on the initial point cloud data by using the target denoising method to obtain denoised point cloud data;

[0101] A projection and boundary extraction unit 2603 is configured to project the denoised point cloud data onto a two-dimensional plane, to determine a target boundary extraction method according to the types and distribution characteristics of the denoised point cloud data, and to screen out a plurality of target boundary points on the projection plane by using the target boundary extraction method;

[0102] A curve fitting unit 2604 is configured to perform curve fitting on the plurality of target boundary points to obtain a boundary point fitting curve;

[0103] A new point cloud data generating unit 2605 is configured to compare the point cloud on the non-fitting curve in the initial point cloud data with the boundary point cloud on the fitting curve, identify points where the initial point cloud differs from the boundary point cloud on the fitting curve as transition point clouds, and combine the transition point clouds with the boundary point clouds on the fitting curve to obtain new point cloud data.

[0104] The three-dimensional model construction unit 2606 is used to use three-dimensional solid modeling software to import the new point cloud data, perform cylindrical surface construction, and generate a three-dimensional model of the drug column.

[0105] An embodiment of the present invention may also provide a device for reconstructing a three-dimensional model of an assembled charge, the device comprising a processor and a memory:

[0106] The memory is used to store program code and transmit the program code to the processor;

[0107] The processor is used to execute the steps of the above-mentioned method for reconstructing the three-dimensional model of the assembled grain according to the instructions in the program code.

[0108] like Figure 27 As shown, an apparatus for reconstructing a three-dimensional model of assembled charge provided by an embodiment of the present invention may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 communicate with each other via the communication bus 13.

[0109] In the embodiment of the present invention, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices.

[0110] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the method for reconstructing a three-dimensional model of assembled grains.

[0111] The memory 11 is used to store one or more programs. The programs may include program codes, which include computer operating instructions. In the embodiment of the present invention, the memory 11 stores at least a program for implementing the following functions:

[0112] Obtain the three-dimensional initial point cloud data of the drug column generated by MATLAB software using end-face shooting;

[0113] determining a target denoising method according to the characteristics and noise type of the initial point cloud data, and performing denoising processing on the initial point cloud data using the target denoising method to obtain denoised point cloud data;

[0114] Project the denoised point cloud data onto a two-dimensional plane, determine a target boundary extraction method according to the type and distribution characteristics of the denoised point cloud data, and screen out a plurality of target boundary points on the projection plane by using the target boundary extraction method;

[0115] Curve fitting is performed on the plurality of target boundary points to obtain a boundary point fitting curve;

[0116] The point cloud on the initial point cloud data that is not on the fitting curve is compared with the boundary point cloud on the fitting curve, different points of the initial point cloud and the boundary point cloud on the fitting curve are found out as transition point cloud, and the transition point cloud and the boundary point cloud on the fitting curve are combined to obtain new point cloud data;

[0117] A three-dimensional entity modeling software is used to import the new point cloud data, perform cylinder construction, and generate a three-dimensional model of the drug column.

[0118] In addition, the memory 11 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device or other volatile solid-state storage device.

[0119] The communication interface 12 can be an interface of a communication module, used for connecting with other devices or systems.

[0120] Of course, it needs to be explained that, Figure 27 The structure shown does not constitute a limitation on the equipment for reconstructing and assembling a three-dimensional model of a drug column in the embodiments of the present application, and in actual application, the equipment for reconstructing and assembling a three-dimensional model of a drug column can include more or fewer components than Figure 27 those shown, or some components can be combined.

[0121] The embodiments of the present application can also provide a computer readable storage medium for storing program codes, the program codes being used to execute the steps of the above-mentioned method for reconstructing and assembling a three-dimensional model of a drug column.

[0122] It needs to be explained that, in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between the entities or operations. Moreover, the terms “include”, “contain” or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitation, the element defined by the statement “including a…” does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0123] Those skilled in the art can clearly understand the application by the description of the above embodiments. The technical solutions of the application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions of the application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0124] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments. In particular, the system or the system embodiment is basically similar to the method embodiment, and thus is described more simply. The related parts can be referred to the part of the method embodiment. The system and the system embodiment described above are merely illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, can be located in one place or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to the actual needs. Those skilled in the art can understand and implement without creative labor.

[0125] The above description is merely preferred embodiments of the application, but not for limiting the protection range of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection range of the application.

Claims

1. A method for reconstructing a three-dimensional model of an assembled grain, characterized in that: include: Obtain the three-dimensional initial point cloud data of the drug column generated by MATLAB software using end-face shooting; determining a target denoising method according to the characteristics and noise type of the initial point cloud data, and performing denoising processing on the initial point cloud data using the target denoising method to obtain denoised point cloud data; Projecting the denoised point cloud data onto a two-dimensional plane, determining a target boundary extraction method based on the type and distribution characteristics of the denoised point cloud data, and screening out a number of target boundary points on the projection surface using the target boundary extraction method; Performing curve fitting on a plurality of target boundary points to obtain a boundary point fitting curve; Comparing the point cloud on the non-fitting curve in the initial point cloud data with the boundary point cloud on the fitting curve, finding the points that are different between the initial point cloud and the boundary point cloud on the fitting curve as transition point clouds, and combining the transition point clouds with the boundary point clouds on the fitting curve to obtain new point cloud data; Use three-dimensional solid modeling software to import the new point cloud data, perform cylindrical surface construction, and generate a three-dimensional model of the drug column.

2. The method for reconstructing a three-dimensional model of assembled grain according to claim 1, characterized in that: The target denoising method includes any one of a radius outlier removal algorithm, a statistical outlier removal algorithm, a bilateral filtering method, and a voxel grid filtering method.

3. The method for reconstructing a three-dimensional model of assembled grain according to claim 2, characterized in that: The target denoising method includes a radius outlier removal algorithm.

4. The method for reconstructing a three-dimensional model of assembled grain according to claim 1, characterized in that: The point cloud data is projected onto a two-dimensional plane using the orthogonal projection method.

5. The method for reconstructing a three-dimensional model of assembled grain according to claim 1, characterized in that: The target boundary extraction method includes any one of a convex hull algorithm and an Alpha shape algorithm.

6. The method for reconstructing a three-dimensional model of assembled grain according to claim 5, characterized in that: The target boundary extraction method includes a convex hull algorithm to find the boundary points of the smallest convex polygon or convex polyhedron that surrounds all point cloud data as the approximate boundary of the zero-charge column.

7. The method for reconstructing a three-dimensional model of assembled grain according to claim 1, characterized in that: A least square method is used to perform curve fitting on a number of target boundary points to obtain a boundary point fitting curve.

8. The method for reconstructing a three-dimensional model of assembled grain according to claim 1, characterized in that: When using three-dimensional solid modeling software to construct digital models, any one of the mesh surface reconstruction method, implicit surface reconstruction method, and parametric surface reconstruction method is adopted.

9. A device for reconstructing and assembling a three-dimensional model of a grain, characterized in that: The device for executing the method for reconstructing a three-dimensional model of assembled grain according to any one of claims 1 to 8 comprises: An initial point cloud acquisition unit is used to acquire three-dimensional initial point cloud data of the drug column generated by MATLAB software using an end-face shooting method; a denoising unit, configured to determine a target denoising method according to the characteristics of the initial point cloud data and the noise type, and perform denoising processing on the initial point cloud data using the target denoising method to obtain denoised point cloud data; a projection and boundary extraction unit, configured to project the denoised point cloud data onto a two-dimensional plane, determine a target boundary extraction method based on the type and distribution characteristics of the denoised point cloud data, and screen out a number of target boundary points on the projection surface using the target boundary extraction method; A curve fitting unit, configured to perform curve fitting on a plurality of target boundary points to obtain a boundary point fitting curve; a new point cloud data generating unit, configured to compare the point cloud on the non-fitting curve in the initial point cloud data with the boundary point cloud on the fitting curve, find out the points where the initial point cloud differs from the boundary point cloud on the fitting curve as transition point clouds, and combine the transition point clouds with the boundary point clouds on the fitting curve to obtain new point cloud data; The three-dimensional model construction unit is used to use three-dimensional solid modeling software to import the new point cloud data, perform cylindrical construction, and generate a three-dimensional model of the drug column.

10. A device for reconstructing and assembling a three-dimensional model of a grain, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method for reconstructing a three-dimensional model of an assembled grain according to any one of claims 1 to 8 according to the instructions in the program code.

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