A three-dimensional topography and deformation measurement method based on marker point blanking and extraction

Abstract

This invention discloses a method for measuring 3D topography and deformation based on marker point culling and extraction, belonging to the field of computer vision 3D measurement and computing. The method includes: creating colored marker points on the surface of the object being measured; projecting a grayscale structured light pattern onto the surface of the object; acquiring the structured light pattern modulated on the surface of the object to obtain a texture pattern; extracting the red, green, and blue (RGB) intensity values ​​of the background and the colored marker points from the texture pattern; calculating the culling coefficient and extraction coefficient based on the RGB intensity values ​​of the background and the colored marker points, and extracting the stripe pattern and marker point pattern respectively; reconstructing the 3D topography using the stripe pattern; associating the preceding and following 3D point clouds using the marker point pattern; and subtracting the corresponding coordinates of the 3D points to obtain 3D deformation information. This method achieves full-field 3D topography reconstruction of complex structures; ensures the accuracy of deformation tracking; and enables 3D topography and deformation measurement of complex objects.

Description

Technical Field

[0001] This invention relates to the field of computer vision 3D measurement and calculation, and mainly to a 3D shape and deformation measurement method based on marker point culling and extraction. Background Technology

[0002] The continuous improvement in reconstruction efficiency and measurement robustness has enabled existing fringe projection technology to achieve efficient and high-precision 3D topography measurement in complex dynamic scenes. However, since the projected pattern is not attached to the object surface, it is impossible to accurately establish the point-to-point mapping relationship of the reconstructed 3D topography information at different times, thus making it impossible to accurately calculate the displacement and deformation of the measured surface.

[0003] In the field of experimental mechanics, digital image correlation is a recognized method for analyzing the deformation and mechanical properties of objects. Digital image correlation utilizes the natural texture of the object's surface or artificially created markers to achieve accurate point-to-point tracking by calculating the degree of grayscale correlation before and after deformation of the analysis area.

[0004] The fringe projection profilometry technique, capable of acquiring dense 3D topography of a scene, and digital image correlation (DIR) technology, enabling accurate deformation tracking, have been combined to form a new method for simultaneous measurement of 3D topography and deformation. Fringe projection profilometry requires sufficiently uniform reflectivity of the measured surface to ensure accuracy in topography measurement; while DIR technology requires the measured surface to provide high-contrast texture information to ensure accurate image matching and deformation calculation. In fringe projection profilometry and DIR technology, one requires extracting marker points from the patterned surface, while the other does not; therefore, their requirements for surface texture clarity are contradictory. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing digital image correlation techniques, which require the measured surface to provide high-contrast texture information to ensure image matching accuracy and deformation calculation precision, resulting in a contradiction between the two requirements for surface texture clarity. This invention provides a three-dimensional topography and deformation measurement method based on marker point culling and extraction.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] A method for measuring three-dimensional topography and deformation based on marker point culling and extraction includes the following steps:

[0008] S1: Create colored markers on the surface of the object being measured;

[0009] S2: Project a grayscale structured light pattern onto the surface of the object being measured;

[0010] S3: Acquire the structured light pattern modulated on the surface of the object under test, and obtain the texture pattern of the surface of the object under test;

[0011] S4: Extract the red, green, and blue channel intensity values ​​of the background and the colored markers in the texture pattern respectively. Calculate the blanking coefficient and extraction coefficient based on the red, green, and blue channel intensity values ​​of the colored markers and the background respectively. Extract the stripe pattern by multiplying the red, green, and blue channel intensity values ​​of the structured light pattern with the blanking coefficient. Extract the marker pattern by multiplying the red, green, and blue channel intensity values ​​of the texture pattern with the extraction coefficient.

[0012] S5: Reconstruct the three-dimensional shape using the stripe pattern, associate the front and rear three-dimensional point clouds using the marker point pattern, and obtain the three-dimensional deformation information by subtracting the point-to-point three-dimensional coordinates of the corresponding coordinate points.

[0013] By employing the above technical solution, marking points are created on the object's surface. The proposed color fading and extraction model is used to simultaneously extract accurate stripe and texture information. This assists in the point-to-point, robust stripe projection measurement method to achieve full-field 3D topography reconstruction of complex structures. This solves the contradiction between the requirements for the clarity of marking points on the object's surface in stripe projection and digital image correlation. It can assist in the measurement of 3D topography deformation of complex objects. No additional pattern projection is required. The texture pattern in the phase-shift image is extracted by modulation and grayscale enhancement is performed, ensuring the accuracy of deformation tracking.

[0014] As a preferred embodiment of the present invention, step S1 includes: characterizing reflectivity by intensity modulation of the colored markers, representing the color difference between the object and the colored markers by the Euclidean distance between the surface of the object under test and the colored markers in the color space, and calculating intensity modulation and color difference by multi-parameter optimization with boundary constraints.

[0015] As a preferred embodiment of the present invention, the intensity modulation and color difference are calculated based on the following formula:

[0016]

[0017] Among them, I tr I tg I tb I represents the red, green, and blue channel intensity values ​​of the colored marker points, respectively. br I bg I bb M represents the intensity values ​​of the red, green, and blue channels of the background, respectively. I D represents the intensity modulation of the colored marker points. C This represents the Euclidean distance between the object and the color marker in the color space. Multi-parameter optimization with boundary constraints can simultaneously ensure M...I and D C The optimal speckle color should be as large as possible.

[0018] As a preferred embodiment of the present invention, the grayscale structured light pattern in step S2 is sequentially projected onto the surface of the object under test using a projector in grayscale mode, in order to reconstruct the three-dimensional topographic distribution of the surface of the object under test.

[0019] As a preferred embodiment of the present invention, the texture pattern of the surface of the object under test in step S3 is obtained by using the following modulation formula:

[0020]

[0021] Where (x,y) represents the two-dimensional image coordinates, and I1I2I3 represents the three-step phase shift pattern at a certain moment.

[0022] As a preferred embodiment of the present invention, in step S4, the blanking coefficient is calculated by the following formula:

[0023]

[0024] Where, σ K I represents the total noise level after conversion. m This indicates the grayscale value of the converted image. Select I. m / σ K K1, K2, and K3 at their maximum values ​​are used as the final hidden surface removal coefficients, σ r ,σ g, σ b These represent the noise levels of the red, green, and blue channels in the texture pattern extracted by modulation, respectively.

[0025] As a preferred embodiment of the present invention, in step S4, the extraction coefficient is calculated by the following formula:

[0026]

[0027] Where ΔI is the difference between the intensity of the marker point and the background after the coefficients are extracted and transformed, and σ T To represent the total noise level after conversion, choose ΔI / σ. T T1, T2, and T3 at their maximum values ​​are used as the final extraction coefficients.

[0028] As a preferred embodiment of the present invention, the stripe pattern is extracted in step S4 by multiplying the red, green and blue channel intensity values ​​of all color structured light patterns by the blanking coefficients K1, K2 and K3 respectively to obtain the stripe image.

[0029] The marker pattern is extracted by multiplying the intensity values ​​of the red, green, and blue channels of all colored texture patterns by extraction coefficients T1, T2, and T3 respectively to obtain the marker image.

[0030] As a preferred embodiment of the present invention, in step S5, the three-dimensional deformation information is acquired by dividing the reconstructed three-dimensional information into three directions: UV, W, and the UV-W deformation calculation formula is as follows:

[0031]

[0032] Where x and y are two-dimensional image coordinates, and X, Y, and Z are the three-dimensional world coordinates of the corresponding two-dimensional points in the reference image and the deformed image.

[0033] On the other hand, an electronic device is disclosed, including at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the method described in any of the above-mentioned embodiments.

[0034] Compared with existing technologies, the beneficial effects of this invention are as follows: by creating marker points on the surface of an object, accurate stripe and texture information can be extracted simultaneously using the proposed color fading and extraction model. This assists in the point-to-point, robust stripe projection measurement method to achieve full-field three-dimensional topography reconstruction of complex structures, solving the contradiction between the requirements for the clarity of marker points on the object surface in stripe projection and digital image correlation. It can assist in the measurement of three-dimensional topography deformation of complex objects. Without the need to project additional patterns, the texture pattern in the phase-shift image is extracted by modulation and grayscale enhancement processing is performed, ensuring the accuracy of deformation tracking. Attached Figure Description

[0035] Figure 1 This is a flowchart of a three-dimensional topography and deformation measurement method based on marker point culling and extraction, as described in Embodiment 1 of the present invention.

[0036] Figure 2 This is a schematic diagram illustrating the calculation of the hidden point elimination coefficient and extraction coefficient in a three-dimensional topography and deformation measurement method based on marker point elimination and extraction as described in Embodiment 1 of the present invention.

[0037] Figure 3 This is a schematic diagram of a projection sequence image of a three-dimensional topography and deformation measurement method based on marker point culling and extraction as described in Embodiment 2 of the present invention.

[0038] Figure 4 This is a physical image showing the results of the hidden point removal and extraction of a three-dimensional topography and deformation measurement method based on marker point removal and extraction as described in Embodiment 2 of the present invention.

[0039] Figure 5 This is a physical three-dimensional shape and deformation result image of a three-dimensional shape and deformation measurement method based on marker point culling and extraction as described in Embodiment 2 of the present invention.

[0040] Figure 6 This is a structural block diagram of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0042] Example 1

[0043] A method for measuring 3D topography and deformation based on marker point culling and extraction, such as Figure 1 As shown, it includes the following steps:

[0044] S1: Create colored markers on the surface of the object being measured;

[0045] Step S1 includes: characterizing reflectivity by intensity modulation of the colored markers, representing the color difference between the object and the colored markers by the Euclidean distance between the surface of the object under test and the colored markers in the color space, and calculating intensity modulation and color difference through boundary constraint multi-parameter optimization.

[0046] The intensity modulation and color difference are calculated based on the following formula:

[0047]

[0048] Among them, I tr I tg I tb I represents the red, green, and blue channel intensity values ​​of the colored marker points, respectively. br I bg I bb M represents the intensity values ​​of the red, green, and blue channels of the background, respectively. I D represents the intensity modulation of the colored marker points. C This represents the Euclidean distance between the object and the color marker in the color space. Multi-parameter optimization with boundary constraints can simultaneously ensure M... I and D C The optimal speckle color should be as large as possible.

[0049] Obtain the color of the marker point that simultaneously satisfies high reflectivity and large color difference, and then apply this color marker point to the surface of the object to be tested.

[0050] S2: Project a grayscale structured light pattern onto the surface of the object being measured;

[0051] The grayscale structured light pattern in step S2 is projected sequentially onto the surface of the object under test using a projector in grayscale mode, in order to reconstruct the three-dimensional topographic distribution of the surface of the object under test.

[0052] Specifically, the projected grayscale structured light pattern can use dual-frequency phase-shift patterns. The high-frequency phase-shift pattern can be represented as I1, I2, and I3. The low-frequency phase-shift pattern can be represented as I4, I5, and I6.

[0053] Arranging the low-frequency phase-shift pattern after the high-frequency phase-shift pattern generates a projection sequence S as: S = I1I2I3I4I5I6; I1I2I3I4I5I6; ...

[0054] This grayscale structured light pattern is projected onto the surface of the object to be measured in a loop using a grayscale projector.

[0055] S3: Acquire the structured light pattern modulated on the surface of the object under test to obtain the texture pattern of the surface of the object under test;

[0056] Specifically, the projected sequence is modulated and deformed by the object under test, and the deformed sequence of images is simultaneously captured by a high-speed color camera. The modulation is used to obtain the texture image in each set of fringe patterns I1I2I3:

[0057]

[0058] Where (x,y) represents the two-dimensional image coordinates, and I1I2I3 represents the three-step phase shift pattern at a certain moment.

[0059] S4: Extract the red, green, and blue channel intensity values ​​of the background and the colored markers in the texture pattern respectively. Calculate the blanking coefficient and extraction coefficient based on the red, green, and blue channel intensity values ​​of the colored markers and the background respectively. Extract the stripe pattern by multiplying the red, green, and blue channel intensity values ​​of the structured light pattern with the blanking coefficient. Extract the marker pattern by multiplying the red, green, and blue channel intensity values ​​of the texture pattern with the extraction coefficient.

[0060] In step S4, the blanking coefficient is calculated using the following formula:

[0061]

[0062] Where, σ K I represents the total noise level after conversion. m This indicates the grayscale value of the converted image. Select I. m / σ K K1, K2, and K3 at their maximum values ​​are used as the final hidden surface removal coefficients, σ r ,σg ,σ b These represent the noise levels of the red, green, and blue channels in the texture pattern extracted by modulation, respectively.

[0063] In step S4, the extraction coefficient is calculated using the following formula:

[0064]

[0065] Where ΔI is the difference between the intensity of the marker point and the background after the coefficients are extracted and transformed, and σ T To represent the total noise level after conversion, choose ΔI / σ. T T1, T2, and T3 at their maximum values ​​are used as the final extraction coefficients.

[0066] The stripe pattern is extracted in step S4 by multiplying the intensity values ​​of the red, green, and blue channels of all colored structured light patterns by blanking coefficients K1, K2, and K3 respectively to obtain the stripe image.

[0067] The marker point pattern is extracted in step S4 by multiplying the intensity values ​​of the red, green, and blue channels of all colored texture patterns by extraction coefficients T1, T2, and T3 respectively to obtain the marker point image.

[0068] S5: Reconstruct the three-dimensional shape using the stripe pattern, associate the front and rear three-dimensional point clouds using the marker point pattern, and obtain the three-dimensional deformation information by subtracting the point-to-point three-dimensional coordinates of the corresponding coordinate points.

[0069] Specifically, the high-quality stripe pattern extracted is used to reconstruct the three-dimensional morphology of the measured surface. The high-contrast marker pattern extracted is used to associate the three-dimensional point clouds before and after the extraction using digital image correlation technology. The three-dimensional deformation information is obtained by subtracting the three-dimensional coordinates of the corresponding coordinate points point by point.

[0070] Furthermore, the step of acquiring the three-dimensional deformation information is as follows:

[0071] The reconstructed 3D information is divided into three directions: UV, W, and the UVW deformation calculation formula is as follows:

[0072]

[0073] Where x and y are two-dimensional image coordinates, and X, Y, and Z are the three-dimensional world coordinates of the corresponding two-dimensional points in the reference image and the deformed image.

[0074] By employing the above technical solution, marking points are created on the object's surface. The proposed color fading and extraction model is used to simultaneously extract accurate stripe and texture information. This assists in the point-to-point, robust stripe projection measurement method to achieve full-field 3D topography reconstruction of complex structures. This solves the contradiction between the requirements for the clarity of marking points on the object's surface in stripe projection and digital image correlation. It can assist in the measurement of 3D topography deformation of complex objects. No additional pattern projection is required. The texture pattern in the phase-shift image is extracted by modulation and grayscale enhancement is performed, ensuring the accuracy of deformation tracking.

[0075] Example 2

[0076] This embodiment is a specific implementation of Example 1, measuring the surface of complex particle foam insoles. Specifically, black and white binary Gray code is used as the structured light encoding map of this invention, and a 24-bit high-speed color camera is selected. The specific process is as follows:

[0077] S1, Create colored markings on the surface of the insole;

[0078] Specifically, the color of the marker to be made is related to the color of the insole surface. The marker color must simultaneously satisfy the requirements of high reflectivity and a large color difference from the insole itself. The reflectivity of the marker is expressed by an intensity formula, and the color difference between the insole and the marker is expressed by the Euclidean distance between the insole and the marker in the color space. Specifically, the reflectivity of the marker and the Euclidean distance between the object and the marker in the color space are expressed by the following formulas:

[0079]

[0080] Among them, I tr I tg I tb I represents the red, green, and blue channel intensity values ​​of the marker point. br I bg I bb The M value represents the red, green, and blue intensity values ​​of the insole surface. I D represents the intensity modulation of the marker point. C This represents the Euclidean distance between the surface color of the insole and the color of the marker point in the color space.

[0081] Obtain a marker color that simultaneously satisfies high reflectivity and significant color difference. The marker color obtained based on the insole color is a light yellow with total internal reflection in the red-green channel. Apply this color marker to the object surface.

[0082] S2. Project a grayscale structured light pattern onto the surface of the object being measured;

[0083] Specifically, the grayscale structured light pattern includes a three-step phase-shifting pattern and a binary Gray code encoding pattern. The three-step phase-shifting pattern includes I1, I2, and I3, with a period of 64. The binary Gray code encoding pattern consists of 6 patterns (GC1, GC2, GC3, GC4, GC5, and GC6). Each fringe pattern is represented by the following formula:

[0084]

[0085] Where A(x,y) is the background light intensity, and B(x,y) is the stripe pattern modulation. The phase represents the phase that carries information about the surface shape of an object.

[0086] Binary Gray code patterns (GC1, GC2, GC3, GC4, GC5, GC6) are interspersed within each group of three-step phase-shift patterns, generating a sequence S as follows: Figure 3 As shown, it can be represented as:

[0087] S=I1I2I3GC1;I1I2I3GC2;I1I2I3GC3;I1I2I3GC4;I1I2I3GC5;I1I2I3GC6;I1I2I3GC1;I1I2I3GC2;...

[0088] S3, a color camera captures the grayscale structured light pattern modulated on the surface of the insole, and uses the modulation to obtain the texture pattern in the phase-shifted pattern;

[0089] Specifically, the projected grayscale structured light pattern is modulated and deformed by the insole surface, and a sequence of deformed images is simultaneously captured by a high-speed color camera. The modulation is used to obtain the texture image in each set of stripe patterns I1I2I3:

[0090]

[0091] Where (x,y) represents the two-dimensional image coordinates, and I1I2I3 represents the three-step phase shift pattern at a certain moment.

[0092] S4, calculate the blanking coefficient and extraction coefficient to obtain the blanking structured light pattern and the extracted texture pattern;

[0093] Specifically, the red, green, and blue channel values ​​I of the object's surface are obtained from the texture pattern obtained by modulation. br I bg and I bb and the red, green and blue three-channel intensity I of the marked points on the object surface tr I tg and I tb , σ K I represents the total noise level after conversion. m This indicates the grayscale value of the converted image. Select I. m / σK K1, K2, and K3 at their maximum values ​​are used as the final hidden surface removal coefficients. The formula for optimizing and extracting the hidden surface removal coefficients is as follows:

[0094]

[0095] Where, σ r ,σ g, σ b These represent the noise levels of the red, green, and blue channels in the texture pattern extracted by modulation, respectively.

[0096] The steps for calculating the extraction coefficients are as follows: Select the converted grayscale image, where ΔI is the difference in intensity between the marker point and the background after the extraction coefficient conversion, and σ... T To represent the total noise level after conversion, choose ΔI / σ. T T1, T2, and T3 at their maximum values ​​are used as the final extraction coefficients. The formula for optimizing the extraction coefficients is as follows:

[0097]

[0098] The red, green, and blue channel intensity values ​​of all colored grayscale structured light patterns are multiplied by the blanking coefficients K1, K2, and K3 respectively to obtain blanking stripe images, and the red, green, and blue channel intensity values ​​of all colored texture patterns are multiplied by the blanking coefficients T1, T2, and T3 respectively to obtain extracted texture images.

[0099] Marker point fading grayscale structured light pattern and extracted texture pattern, such as Figure 4 As shown.

[0100] S5 uses stripe patterns to reconstruct the three-dimensional shape and uses marker point patterns to achieve three-dimensional deformation tracking.

[0101] Specifically, the high-quality stripe pattern extracted is used to reconstruct the three-dimensional morphology of the measured surface. The high-contrast marker pattern extracted is used to associate the three-dimensional point clouds before and after the extraction using digital image correlation technology. The three-dimensional deformation information is obtained by subtracting the three-dimensional coordinates of the corresponding coordinate points point by point.

[0102] Furthermore, the step of acquiring the three-dimensional deformation information is as follows:

[0103] The reconstructed 3D information is divided into three directions: UV, W, and the UVW deformation calculation formula is as follows:

[0104]

[0105] Where x and y are two-dimensional image coordinates, and X, Y, and Z are the three-dimensional world coordinates of the corresponding two-dimensional points in the reference image and the deformed image.

[0106] Three-dimensional morphology and deformation results are shown in the figure. Figure 5 As shown.

[0107] Example 3

[0108] like Figure 6 As shown, an electronic device includes at least one processor, a memory communicatively connected to the at least one processor, and at least one input / output interface communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform a method described in the foregoing embodiments. The input / output interface may include a display, keyboard, mouse, and USB interface for inputting and outputting data.

[0109] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0110] When the integrated units of this invention are implemented as software functional units and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring three-dimensional topography and deformation based on marker point culling and extraction, characterized in that, Includes the following steps: S1: Create colored markers on the surface of the object being measured; S2: Project a grayscale structured light pattern onto the surface of the object being measured; S3: Acquire the structured light pattern modulated on the surface of the object under test, and obtain the texture pattern of the surface of the object under test; S4: Extract the red, green, and blue channel intensity values ​​of the background and the colored markers in the texture pattern respectively. Calculate the blanking coefficient and extraction coefficient based on the red, green, and blue channel intensity values ​​of the colored markers and the background respectively. Extract the stripe pattern by multiplying the red, green, and blue channel intensity values ​​of the structured light pattern with the blanking coefficient. Extract the marker pattern by multiplying the red, green, and blue channel intensity values ​​of the texture pattern with the extraction coefficient. S5: Reconstruct the three-dimensional shape using the stripe pattern, use the marker point pattern to complete the association between the front and rear three-dimensional point clouds, and subtract the point-to-point three-dimensional coordinates of the corresponding coordinate points to obtain three-dimensional deformation information. Step S1 includes: characterizing reflectivity using the intensity modulation of the colored markers; representing the color difference between the object and the colored markers using the Euclidean distance between the surface of the measured object and the colored markers in the color space; and calculating the intensity modulation and color difference through boundary constraint multi-parameter optimization; and calculating the intensity modulation and color difference based on the following formula: Among them, I tr I tg I tb I represents the red, green, and blue channel intensity values ​​of the colored marker points, respectively. br I bg I bb M represents the intensity values ​​of the red, green, and blue channels of the background, respectively. I D represents the intensity modulation of the colored marker points. C This represents the Euclidean distance between the object and the color marker in the color space. Multi-parameter optimization with boundary constraints can simultaneously ensure M... I and D C The largest possible optimal speckle color; In step S4, the blanking coefficient is calculated using the following formula: in, This indicates the total noise level after conversion, and Im represents the grayscale value of the converted image. Selecting Im / K1, K2, and K3 at their maximum values ​​are used as the final hidden surface removal coefficients. , , These represent the noise levels of the red, green, and blue channels in the texture pattern obtained by modulation extraction, respectively. In step S4, the extraction coefficient is calculated using the following formula: in, To extract the difference in intensity between the marker points and the background after coefficient transformation, This indicates the total noise level after conversion. (Select) T1, T2, and T3 at the maximum value are used as the final extraction coefficients; The stripe pattern is extracted in step S4 by multiplying the intensity values ​​of the red, green and blue channels of all colored structured light patterns by the blanking coefficients K1, K2 and K3 respectively to obtain the stripe image. The marker pattern is obtained by multiplying the intensity values ​​of the red, green, and blue channels of all colored texture patterns by extraction coefficients T1, T2, and T3, respectively.

2. The method for measuring three-dimensional topography and deformation based on marker point culling and extraction according to claim 1, characterized in that, The grayscale structured light pattern in step S2 is projected sequentially onto the surface of the object under test using a projector in grayscale mode, in order to reconstruct the three-dimensional topographic distribution of the surface of the object under test.

3. The method for measuring three-dimensional topography and deformation based on marker point culling and extraction according to claim 1, characterized in that, In step S3, the texture pattern of the surface of the object under test is obtained using the following modulation formula: Where (x,y) represents the two-dimensional image coordinates, and I1I2I3 represents the three-step phase shift pattern at a certain moment.

4. The method for measuring three-dimensional topography and deformation based on marker point culling and extraction according to claim 1, characterized in that, In step S5, the 3D deformation information is acquired by dividing the reconstructed 3D information into three directions: UV, W, and the UV-W deformation calculation formula is as follows: Where x and y are two-dimensional image coordinates, and X, Y, and Z are the three-dimensional world coordinates of the corresponding two-dimensional points in the reference image and the deformed image.

5. An electronic device, characterized in that, The method includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.

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