Monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection

Abstract

The present application discloses a monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection, which belongs to the field of image processing technology. The method includes: obtaining a structured light stripe image based on the surface of a highly reflective object, obtaining multiple multi-frequency polarization stripe images at different angles based on the structured light stripe image, and determining the overexposed area of ​​each multi-frequency polarization stripe image; constructing a mask image of the multi-frequency polarization stripe image based on the overexposed area and obtaining the corresponding highlight image, constructing a corresponding Gaussian pyramid image sequence and a Laplace pyramid image sequence, constructing a pyramid fusion image based on multiple Gaussian pyramid image sequences and multiple Laplace pyramid image sequences, and obtaining a final reconstructed image based on the fused pyramid image, merging the final reconstructed image with the structured light stripe image, and obtaining a target three-dimensional image. The present application can effectively solve the problem that some detailed features are difficult to restore in the face of overexposure.

Description

Technical Field

[0001] The present application belongs to the field of image processing technology, and specifically relates to a monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection. Background Art

[0002] Structured light 3D imaging technology is an advanced machine vision technology that has shown wide application potential in many fields, from 3D positioning and grasping of robot vision systems, to 3D measurement and quality control of parts in industrial inspection, to 3D human body scanning and auxiliary diagnosis in the medical field, which has brought many conveniences and innovations to people's lives and work. The principle of structured light 3D imaging technology is based on optical triangulation. By projecting a known pattern of structured light onto the surface of the object to be measured, the camera is then used to capture the deformed image formed by these light patterns on the surface of the object. By analyzing the displacement or phase change of the light pattern in the deformed image, combined with the geometric relationship between the projector and the camera, the 3D coordinates of each point on the surface of the object can be calculated, thereby realizing 3D reconstruction of the surface of the object.

[0003] However, when dealing with objects with high reflectivity (such as metal, mirrored glass, etc.), the intensity of reflected light is much higher than diffuse light, which can easily cause severe overexposure. Overexposure can cause the pixel values ​​in certain areas of the image to reach saturation, resulting in loss of image details, seriously affecting the accuracy and integrity of 3D reconstruction. Currently, traditional approaches to the overexposure problem on the surface of highly reflective objects include adjusting the camera's exposure time or gain to balance the exposure values ​​of areas with different reflectivity, or using optical filters to reduce the intensity of reflected light, but the effect is limited when the image details are severely lost. Therefore, in the case of loss of detail features due to overexposure, some detail features will be difficult to restore. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection, which can effectively solve the problem that some detailed features are difficult to restore in the face of overexposure.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a monocular structured light three-dimensional imaging method based on multi-frequency polarization projection, the method comprising:

[0007] Acquire structured light stripe images based on the surface of highly reflective objects;

[0008] Acquire multiple multi-frequency polarization fringe images at different angles according to the structured light fringe image;

[0009] The over-exposed area of ​​each multi-frequency polarization fringe image is determined by local gray value analysis method;

[0010] Constructing a mask image of a multi-frequency polarization fringe image based on the overexposed area;

[0011] Obtaining a corresponding highlight image according to the multi-frequency polarization fringe image and the mask image;

[0012] According to the highlight image, a corresponding Gaussian pyramid image sequence is constructed;

[0013] Based on the Gaussian pyramid image sequence, a corresponding Laplacian pyramid image sequence is constructed;

[0014] Based on multiple Gaussian pyramid image sequences and multiple Laplacian pyramid image sequences, a pyramid fusion image is constructed; wherein the topmost image of the fusion pyramid image is a fusion image of the topmost images of the multiple Gaussian pyramid image sequences, and the second topmost to the bottommost images of the fusion pyramid image are fusion images of corresponding layers of the multiple Laplacian pyramid image sequences;

[0015] According to the fused pyramid image, a final reconstructed image is obtained;

[0016] The final reconstructed image is combined with the structured light fringe image to obtain the target three-dimensional image.

[0017] Furthermore, the step of determining the overexposed area of ​​each multi-frequency polarization fringe image by a local gray value analysis method specifically includes:

[0018] According to the local gray value analysis method, the multi-frequency polarization stripe image is divided into several regional images;

[0019] Calculate the average gray value and adaptive threshold of each area image respectively;

[0020] If the average gray value is greater than the adaptive threshold, the image in this area is determined to be an overexposed area;

[0021] The adaptive threshold is calculated as follows:

[0022] T(x,y)=μ(x,y)-kσ(x,y),

[0023] Where (x, y) represents the position of the regional image, T(x, y) represents the adaptive threshold of the regional image, μ(x, y) represents the average grayscale value of the adjacent regional image, σ(x, y) represents the standard deviation of the pixels of the adjacent regional image, and k represents the adjustment parameter.

[0024] Furthermore, in the step of constructing a mask image of the multi-frequency polarization fringe image based on the overexposed area, it specifically includes:

[0025] Comparing the overexposed area of ​​each multi-frequency polarization stripe image, selecting the maximum overexposed multi-frequency polarization stripe image, wherein the maximum overexposed multi-frequency polarization stripe image is the multi-frequency polarization stripe image with the largest overexposed area;

[0026] Construct a mask image of the same size as the maximum overexposed multi-frequency polarization fringe image;

[0027] In the mask image, the pixel values ​​of the image positions in the overexposed area are set to 1, and the pixel values ​​of the image positions in the non-overexposed area are set to .

[0028] Furthermore, the step of constructing a corresponding Gaussian pyramid image sequence according to the highlight image specifically includes:

[0029] Set the highlight image at the bottom of the Gaussian pyramid image sequence;

[0030] The highlight image is downsampled multiple times to obtain an image sequence with gradually decreasing resolution, and a Gaussian pyramid image sequence corresponding to the highlight image is constructed from bottom to top;

[0031] Among them, the number of layers of the Gaussian pyramid image sequence corresponding to each highlight image is the same.

[0032] Furthermore, the step of constructing a corresponding Laplacian pyramid image sequence based on the Gaussian pyramid image sequence specifically includes:

[0033] Based on the Gaussian pyramid image sequence, a Laplacian pyramid image sequence is constructed starting from the bottom layer;

[0034] The calculation method for constructing the Laplacian pyramid image sequence is:

[0035] L k =G k -expand(G (k+1) ) ,

[0036] Where k represents the number of layers, G k represents the image at the kth layer of the Gaussian pyramid image sequence, G (k+1) represents the image of the (k+1)th layer of the Gaussian pyramid image sequence, expand Indicates interpolation operation. L k Represents the image at the kth level of the Laplacian pyramid image sequence.

[0037] Further, in the step of constructing a fused pyramid image based on multiple Gaussian pyramid image sequences and multiple Laplacian pyramid image sequences, specifically including;

[0038] According to the pixel maximum value fusion principle, the top-most image of multiple Gaussian pyramid image sequences is fused as the top-most image of the fused pyramid image, and the images of the corresponding layers of multiple Laplacian pyramid image sequences are fused as the second top to the bottom images of the fused pyramid;

[0039] The calculation method for the top-level image fusion of multiple Gaussian pyramid image sequences is:

[0040] F K =fuse(G 1K ，G 2K ，......，G nK ) ,

[0041] Where K represents the top layer of the pyramid image sequence, n represents the number of Gaussian pyramid image sequences, n=1,2,3,...,n, G 1K 、G 2K 、G nK Respectively represent the topmost image of each Gaussian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F K represents the top-most fused image;

[0042] The calculation method for image fusion of corresponding layers of multiple Laplacian pyramid image sequences is:

[0043] F k =fuse(L 1k ，L 2k ，......，L nk ) ,

[0044] Where k represents the number of layers, n represents the number of Laplacian pyramid image sequences, n=1,2,3,...,n, L 1k 、 L 2k 、L nk Respectively represent the kth layer of each Laplacian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F krepresents the fused image of the kth layer.

[0045] Furthermore, the step of obtaining the final reconstructed image according to the fused pyramid image specifically includes:

[0046] An upsampling operation is performed on the topmost layer of the fused pyramid image, and the image is added to the fused image at the second top layer to obtain a reconstructed image at the second top layer of the fused pyramid image;

[0047] Starting from the second top layer of the fusion pyramid, a reconstructed image of the current layer is obtained based on the reconstructed image of the previous layer and the fused image of the current layer;

[0048] The reconstructed image is repeatedly obtained until the bottom layer of the fusion pyramid image is reached, and the final reconstructed image is output.

[0049] In a second aspect, an embodiment of the present application provides a monocular structured light three-dimensional imaging system based on multi-frequency polarization projection, the system comprising:

[0050] A structured light image acquisition module, used to acquire a structured light stripe image;

[0051] A multi-frequency polarization stripe image generation module is used to obtain a plurality of multi-frequency polarization stripe images at different angles according to the structured light stripe image;

[0052] A pyramid image sequence construction module is used to determine the overexposed area of ​​each multi-frequency polarization stripe image by a local gray value analysis method, construct a mask image of the multi-frequency polarization stripe image based on the overexposed area, obtain a corresponding highlight image based on the multi-frequency polarization stripe image and the mask image, construct a corresponding Gaussian pyramid image sequence based on the highlight image, and construct a corresponding Laplacian pyramid image sequence based on the Gaussian pyramid image sequence;

[0053] A fused pyramid image construction module is used to construct a fused pyramid image based on multiple Gaussian pyramid image sequences and multiple Laplacian pyramid image sequences; wherein the topmost image of the fused pyramid image is a fused image of the topmost images of the multiple Gaussian pyramid image sequences, and the second topmost to bottommost images of the fused pyramid image are fused images of corresponding layers of the multiple Laplacian image sequences;

[0054] The reconstructed image acquisition module obtains the final reconstructed image based on the fused pyramid image;

[0055] The three-dimensional imaging module is used to merge the final reconstructed image with the structured light fringe image to obtain the target three-dimensional image.

[0056] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method described in the first aspect are implemented.

[0057] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0058] In an embodiment of the present application, a structured light stripe image is first obtained based on the surface of a highly reflective object, and a plurality of multi-frequency polarization stripe images at different angles are obtained based on the structured light stripe image, and information loss that may be caused by a single angle is avoided by collecting from multiple angles; then, the overexposed area of ​​each multi-frequency polarization stripe image is determined by a local gray value analysis method and an adaptive threshold, and a targeted analysis is performed on each local area of ​​the multi-frequency polarization stripe image, so that the tiny details in the image can be captured more keenly; then, a mask image of the multi-frequency polarization stripe image is constructed through the overexposed area, and a highlight image corresponding to the multi-frequency polarization stripe image is obtained, and the overexposed area is marked through the mask image region, accurately segment the highlight image of each multi-frequency stripe image; according to the highlight image, construct the corresponding Gaussian pyramid image sequence and Laplace pyramid image sequence. The Laplace pyramid image sequence can more accurately capture and describe the detail features of the image by layering the detail information of different scales; construct a fusion pyramid according to the pixel maximum method, fuse the Gaussian image sequence corresponding to different highlight images with the Laplace pyramid image sequence, and highlight the brighter area information in the highlight image to the greatest extent; finally, reconstruct through the fusion pyramid, and merge the final reconstructed image with the structured light stripe image to output the target three-dimensional image. This application can effectively solve the problem of difficulty in restoring when detail features are lost due to overexposure. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart showing an implementation method of a monocular structured light three-dimensional imaging method based on multi-frequency polarization projection provided by an embodiment of the present invention is shown;

[0060] Figure 2 The non-polarized point cloud image, the 20° polarized point cloud image, the 40° polarized point cloud image, and the fused point cloud image of 20° and 40° polarization provided by the embodiment of the present invention are shown;

[0061] Figure 3 The non-polarized image, the 20° polarized image, the 40° polarized image, and the fused image of the 20° and 40° polarizations provided by the embodiments of the present invention are shown;

[0062] Figure 4A schematic diagram of the structure of a monocular structured light three-dimensional imaging system based on multi-frequency polarization projection provided by an embodiment of the present invention is shown;

[0063] Figure 5 A schematic diagram showing the structure of a pyramid image sequence provided by an embodiment of the present invention is shown;

[0064] Figure 6 A schematic structural diagram of an electronic device using a monocular structured light three-dimensional imaging method based on multi-frequency polarization projection provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here. In addition, the "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the objects associated with each other are in an "or" relationship.

[0067] In the following, in combination with the accompanying drawings, the monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0068] It is worth noting that in structured light 3D imaging technology, when the external ambient light conditions become extremely complex and changeable, and are filled with light from all directions, different intensities and frequencies, traditional imaging methods are often prone to difficulties. The light pattern projected onto the surface of the object is easily obscured and confused by the ambient light, resulting in deviations in the extraction of depth information. This is because the fine light pattern projected onto the surface of the object is easily submerged or confused by these chaotic ambient lights, resulting in significant deviations in the extraction of depth information, affecting the final 3D reconstruction accuracy. The use of polarization characteristics can effectively reduce the interference of ambient light, making the light pattern projected onto the surface of the object more recognizable, thereby accurately obtaining the depth information of the object and building a more accurate 3D model.

[0069] The monocular structured light 3D imaging method based on multi-frequency polarization projection disclosed in this application can not only meet the imaging requirements of objects of different scales, but also greatly improve the anti-interference ability of imaging. Compared with traditional imaging methods, this method can effectively resist the interference of stray light in the external environment, accurately obtain depth information, and thus construct a highly accurate and detailed 3D model, providing strong technical support for many fields such as industrial manufacturing, cultural relics protection, and virtual reality.

[0070] See also Figures 1 to 3 , shown is an implementation of a monocular structured light three-dimensional imaging method based on multi-frequency polarization projection provided by the first embodiment of the present invention, the method includes steps S101 to S110, wherein:

[0071] S101. Acquire a structured light stripe image based on a highly reflective object surface.

[0072] In this embodiment, the present application establishes a three-dimensional imaging device based on the structured light imaging principle, which includes: a CMOS camera, a DLP projector, a multi-frequency light source, a polarization filter (including a polarizer and an analyzer), a standard sphere and other components. The multi-frequency light source is introduced into the DLP projector through an optical fiber to project a coded structured light stripe pattern, and the polarizer is set in front of the DLP projector and the analyzer is set in front of the camera, and the structured light stripe image information projected onto the surface of the standard sphere is captured by the camera.

[0073] In this embodiment, the multi-frequency light source adopts a halogen light source, and the light source wavelength band is 400-1000nm.

[0074] It is worth noting that the projection scheme adopted in this embodiment is a twelve-step phase shift method and a complementary Gray code. The twelve-step phase shift method can reduce measurement errors and noise interference, improve the accuracy of phase measurement, and has a certain robustness to changes in ambient light and surface reflectivity of objects; the complementary Gray code is improved on the basis of the Gray code, and a specific cyclic suffix is ​​added after the Gray code to make it complement the Gray code, thereby solving the problem of Gray code jumping at the edge. This embodiment combines the twelve-step phase shift method with the complementary Gray code, which can improve the phase unfolding accuracy, thereby achieving high-precision three-dimensional reconstruction.

[0075] S102 . Acquire a plurality of multi-frequency polarization fringe images at different angles according to the structured light fringe image.

[0076] In this embodiment, the angle of the polarizer is kept unchanged, and the analyzer is adjusted to select different angles to obtain multi-frequency polarization stripe images, and each multi-frequency polarization stripe image has a different grayscale level.

[0077] In some embodiments of the present application, the angle of the polarizing filter is adjusted according to the Brewster angle principle. The Fresnel formula provides a theoretical basis and a basis for quantitative calculation for the Brewster angle principle. The size of the Brewster angle and the polarization characteristics of the reflected light at this angle can be accurately derived according to the Fresnel formula.

[0078] It is worth mentioning that the Fresnel formula describes the behavior of light between media with different refractive indices. When light waves pass through the interface between different media, reflection and refraction occur, and the incident light is divided into two parts: reflected light and refracted light.

[0079] Among them, when light is incident from air to the surface of an object, the Fresnel formula is:

[0080] ，

[0081] ,

[0082] in, n 1 and n 2 are the refractive indices of air and objects, and denote the incident angle and the reflection angle, respectively. represents the reflection coefficient of the light vector component perpendicular to the incident surface, Represents the reflection coefficient of the light vector component parallel to the incident surface.

[0083] It is worth noting that when the incident angle is the Brewster angle, the reflection coefficient of the light vector component parallel to the incident surface is 0, thus obtaining the size of the Brewster angle.

[0084] In some embodiments of the present application, different regions of the surface of an object have different roughness, curvature, etc., and different reflection characteristics and polarization angles for light of different frequencies, so it is necessary to capture multi-frequency polarization stripe images at different angles.

[0085] In this embodiment, multi-frequency polarization fringe images are acquired by photographing at polarization angles of 20° and 40° respectively.

[0086] Step S103: determining the overexposed area of ​​each multi-frequency polarization fringe image by using a local gray value analysis method.

[0087] In some embodiments of the present application, the multi-frequency polarization stripe image is divided into several regional images according to the local grayscale value analysis method, and the average grayscale value and adaptive threshold of each regional image are calculated respectively. If the average grayscale value of the regional image is greater than the adaptive threshold, the regional image is judged to be an overexposed area.

[0088] In some embodiments of the present application, the adaptive threshold is calculated as follows:

[0089] T(x,y)=μ(x,y)-kσ(x,y)

[0090] Where (x, y) represents the position of the regional image, T(x, y) represents the adaptive threshold of the regional image, μ(x, y) represents the average grayscale value of the adjacent regional image, σ(x, y) represents the standard deviation of the pixels of the adjacent regional image, and k represents the adjustment parameter.

[0091] In this embodiment, determining the overexposed area of ​​each multi-frequency polarized stripe image by local gray value analysis is a delicate and systematic process. First, the multi-frequency polarized stripe image needs to be preprocessed, including removing noise and performing necessary filtering to ensure that the image quality meets the analysis requirements. Subsequently, the image is converted from color space to gray space in order to extract the gray value of each pixel. Next, the image is divided into multiple small area images using block processing technology, and the average gray value of each area image is calculated. Based on these statistical information, a reasonable overexposure threshold is set by an adaptive threshold algorithm, and the overexposed area in the image can be identified by comparing the gray value of each small area with the threshold.

[0092] It is worth noting that the local gray value analysis method can fully consider the gray characteristics of different areas of the image, can better adapt to the situation where the gray value in the image varies greatly in the local range, can more sensitively capture the tiny details and local features in the image, and avoid the problem of detail loss caused by global threshold processing. When the adaptive threshold is combined with the local gray analysis method, the adaptive threshold can determine the threshold according to the specific gray distribution and characteristics of each local area, making the threshold selection more accurate and targeted, and improving the quality and effect of image processing.

[0093] S104: constructing a mask image of the multi-frequency polarization fringe image based on the overexposed area.

[0094] In some embodiments of the present application, the overexposed area of ​​each multi-frequency polarization stripe image is compared, and the maximum overexposed multi-frequency polarization stripe image is selected, and the maximum overexposed multi-frequency polarization stripe image represents the multi-frequency polarization stripe image with the largest overexposed area; and a mask image of the same size as the maximum overexposed multi-frequency polarization stripe image is constructed.

[0095] In some implementations of the present application, when constructing a mask image, the pixel values ​​of image positions in overexposed areas are set to 1, and the pixel values ​​of image positions in non-overexposed areas are set to 0.

[0096] In this embodiment, according to the overexposed area determined in the above steps, a blank image with the same size as the maximum overexposed multi-frequency polarization stripe image is designed and generated, and its pixel data type is usually a binary type, and all pixel values ​​are set to 0 in the initial state. According to the pixel position of the determined overexposed area, the corresponding pixel value in the mask image is set to 1, indicating that these areas contain the image information of the overexposed area. The pixel value corresponding to the non-overexposed area is set to 0, indicating that these non-overexposed areas will not be processed subsequently.

[0097] It is worth noting that by accurately marking the overexposed area with the mask image, the edge information, texture information, etc. in the multi-frequency polarization stripe image can be retained to the greatest extent, which helps to obtain the characteristics of the object more comprehensively and accurately and conduct subsequent analysis.

[0098] S105 . Acquire a corresponding highlight image according to the multi-frequency polarization fringe image and the mask image.

[0099] In this embodiment, the multi-frequency polarized stripe image and the mask image are multiplied by pixels at corresponding positions to obtain a highlight image, and the area multiplied by the pixel value of 1 in the mask image is retained, and the area multiplied by the pixel value of 0 in the mask image is discarded, thereby separating the overexposed areas of the multi-frequency polarized stripe image at different angles and obtaining the corresponding highlight image.

[0100] It is worth noting that the mask image is applied to retain the detailed features of the overexposed area in the multi-frequency polarization stripe image, so as to obtain the corresponding highlight image. In this process, the mask image is used as an indicator to identify which pixels belong to the valid part of the highlight area and which pixels should be ignored. Through pixel-by-pixel comparison and processing, the highlight image finally generated only contains the highlight areas that have been screened and confirmed in the original multi-frequency polarization stripe image, providing a clear and accurate information basis for subsequent image analysis and feature extraction.

[0101] In this embodiment, highlight images corresponding to the multi-frequency polarization stripe images taken at polarizations of 20° and 40° are obtained respectively.

[0102] S106: Construct a corresponding Gaussian pyramid image sequence according to the highlight image.

[0103] In some embodiments of the present application, the highlight image is set at the bottom layer of the Gaussian pyramid image sequence, the highlight image is downsampled multiple times to obtain an image sequence with gradually decreasing resolution, and a Gaussian pyramid image sequence corresponding to the highlight image is constructed from bottom to top.

[0104] In this embodiment, the number of layers of the Gaussian pyramid image sequence corresponding to each highlight image is the same.

[0105] In this embodiment, the highlight image is first used as the bottom layer of the pyramid (i.e., the highest resolution layer), and then Gaussian smoothing is performed on the image layer to reduce high-frequency noise and details in the image while retaining important structural information. Next, the smoothed image is downsampled to reduce the size of the image by a certain ratio, thereby obtaining the second layer of the pyramid. This process is repeated, and each layer is smoothed and downsampled based on the previous layer until a predetermined number of pyramid layers is reached or the image size is no longer suitable for further downsampling. Finally, an image sequence containing multiple resolution levels is obtained, namely a Gaussian pyramid image sequence.

[0106] It is worth mentioning that Gaussian pyramid is a commonly used technology in the field of image processing. It is essentially a multi-scale representation of signals. By performing Gaussian blurring and down-sampling on the same image multiple times, it provides image representations at different scales from fine to coarse. The Gaussian pyramid image sequence provides multi-scale image data for subsequent feature extraction, enabling the extraction of more representative and robust image features.

[0107] S107: Based on the Gaussian pyramid image sequence, construct a corresponding Laplacian pyramid image sequence.

[0108] In some embodiments of the present application, based on the Gaussian pyramid image sequence, a Laplacian pyramid image sequence is constructed from the bottom layer. The calculation method for constructing the Laplacian pyramid image sequence is:

[0109] L k =G k -expand(G (k+1) ) ,

[0110] Where k represents the number of layers, G k represents the image at the kth layer of the Gaussian pyramid image sequence, G (k+1) represents the image of the (k+1)th layer of the Gaussian pyramid image sequence, expand Indicates interpolation operation. L k Represents the image at the kth level of the Laplacian pyramid image sequence.

[0111] In this embodiment, the construction of the Laplacian pyramid image sequence starts from the bottom layer of the Gaussian pyramid image sequence (i.e., the resolution level of the original highlight image) and proceeds upward layer by layer. At each layer, the image of the current layer in the Gaussian pyramid is first obtained, and then upsampled to the same size as the next layer (i.e., the higher resolution layer). Next, the image of the next layer of the Gaussian pyramid image sequence is subtracted from the upsampled image, and the obtained difference image reflects the detail information at the current scale, that is, the image of the current layer in the Laplacian pyramid image sequence. This process is repeated until all layers of the Gaussian pyramid are traversed, and the Laplacian pyramid image sequence is finally obtained. Each layer in this sequence contains the detailed features of the image at different scales, providing a rich information basis for tasks such as image analysis, feature extraction, image fusion, and multi-scale image processing.

[0112] In this embodiment, the Laplacian pyramid image sequence is derived from the Gaussian pyramid, which is mainly constructed by performing specific operations on images of adjacent layers in the Gaussian pyramid. The core idea is to use the difference information of images at different scales to represent the details of the image. The Laplacian image of each layer can be regarded as the difference between the image of this layer in the corresponding Gaussian pyramid and the image of the previous layer after upsampling and Gaussian filtering, which can better capture the high-frequency detail information of the image.

[0113] It is worth noting that each layer of the Laplacian pyramid image sequence contains high-frequency detail information of the image at that scale. By representing detail information at different scales in layers, it is possible to more accurately capture and describe the detail features of the image, and to operate on these details more specifically without affecting information at other scales. At the same time, the Laplacian pyramid image sequence has a smoothing and suppression effect on noise to a certain extent.

[0114] S108 . Construct a fused pyramid image based on the multiple Gaussian pyramid image sequences and the multiple Laplacian pyramid image sequences.

[0115] In this embodiment, the topmost image of the fused pyramid image is a fused image of topmost images of multiple Gaussian pyramid image sequences, and the second topmost to bottommost images of the fused pyramid image are fused images of corresponding layers of the Laplacian pyramid image sequence.

[0116] In some embodiments of the present application, according to the pixel maximum fusion principle, the top-most image of multiple Gaussian pyramid image sequences is fused as the top-most image of the fused pyramid image, and the images of corresponding layers of multiple Laplacian pyramid image sequences are fused as the second top to bottom images of the fused pyramid image.

[0117] The calculation method for the top-level image fusion of multiple Gaussian pyramid image sequences is:

[0118] F K =fuse(G 1K ，G 2K ，......，G nK ) ,

[0119] Where K represents the top layer of the pyramid image sequence, n represents the number of Gaussian pyramid image sequences, n=1,2,3,...,n, G 1K 、G 2K 、G nK Respectively represent the topmost image of each Gaussian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F K Represents the top-most fused image.

[0120] The calculation method of image fusion of corresponding layers of the multiple Laplacian image sequences is:

[0121] F k =fuse(L 1k ，L 2k ，......，L nk ) ,

[0122] Where k represents the number of layers, n represents the number of Laplacian pyramid image sequences, n=1,2,3,...,n, L 1k 、 L 2k 、L nk Respectively represent the kth layer of each Laplacian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F k represents the fused image of the kth layer.

[0123] In this embodiment, the pixel maximum value fusion principle is mainly based on selecting pixels with larger pixel values ​​at corresponding positions to construct a fused image. That is, when fusing two or more highlight images, for each pixel position in the fused image, the pixel with the largest pixel value at the corresponding position of the highlight image is selected as the pixel value of the fused image at that position.

[0124] It is worth noting that fusing images according to the pixel maximum fusion principle can highlight the brighter areas in the image and better display detail information when processing highlight images. At the same time, the implementation process of the pixel maximum fusion principle is simple, does not require complex mathematical models and a lot of calculations, is easy to understand and implement, and has a fast calculation speed.

[0125] In this embodiment, when the Laplacian pyramid image sequences corresponding to different highlight images are fused, the detailed features of the image can be better preserved, avoiding the problems of unnatural fusion boundaries and loss of details that may occur when directly fusing the original images, thereby achieving a high-quality image fusion effect.

[0126] S109: Obtain a final reconstructed image based on the fused pyramid image.

[0127] In some embodiments of the present application, an upsampling operation is performed on the image at the top layer of the fused pyramid image, and the image is added to the fused image at the next top layer to obtain a reconstructed image at the next top layer of the fused pyramid image; starting from the next top layer of the fused pyramid, a reconstructed image of the current layer is obtained based on the reconstructed image of the previous layer and the fused image of the current layer; the reconstructed images are repeatedly obtained until the bottom layer of the fused pyramid image, and a final reconstructed image is output.

[0128] In this embodiment, the reconstructed image is obtained in the following manner:

[0129] I k =F k +expand(I (k+1) ) ,

[0130] Where k represents the number of layers, F k represents the image at the kth layer of the fused pyramid image, I (k+1) represents the reconstructed image of the (k+1)th layer of the fused pyramid image, expand Indicates interpolation operation. I k represents the reconstructed image of the kth layer.

[0131] In this example, the highlight image is reconstructed by fusing the pyramid image. The reconstructed highlight image combines the useful information of the original image and the highlight image at different scales, improves the performance of the highlight area, and makes the highlight image more in line with the desired effect in terms of details and contrast. By fusing the pyramid image to reconstruct the highlight image, it is possible to make full use of multi-scale information and effectively improve the quality of the highlight image, so that it can achieve a better state in terms of visual effects and detail presentation.

[0132] It is worth mentioning that the highlight images of the multi-frequency polarization stripe images acquired at multiple different angles are reconstructed by fusing the pyramid images. The final reconstructed image not only contains the main structure and low-frequency information of the original image, but also integrates the details and high-frequency features from different source images, thereby achieving comprehensive, accurate and high-quality reconstruction of the image information.

[0133] S110 , combining the final reconstructed image with the structured light fringe image to obtain a target three-dimensional image.

[0134] In this embodiment, the final reconstructed image is combined with the structured light fringe image to obtain a three-dimensional point cloud image and output a target three-dimensional image.

[0135] In this embodiment, if Figure 2 As shown in the figure, there are point cloud images without polarization, point cloud images with 20° polarization, point cloud images with 40° polarization, and fused point cloud images with 20° and 40° polarization. It can be seen that the point cloud information of the strong reflection area is completely lost without polarization, and the information loss is improved after polarization at a certain angle, but the point cloud information of the strong reflection area and the dark edge is still lost to varying degrees. By fusing and reconstructing the highlight images of the polarized stripe images at multiple angles, the detailed features of the highlight area can be well preserved.

[0136] In this embodiment, if Figure 3 As shown in the figure, there are non-polarized images, 20° polarized images, 40° polarized images, and fused images of 20° and 40° polarizations. It can be seen that in the case of non-polarization, the phase information of the overexposed area is severely lost; in the case of polarization of 20° and 40°, the degree of filtering of strong reflections is different at different polarization angles, the brightness of the overexposed area is weakened, but the overall brightness is also reduced, and some edge phase information is lost. In the case of multi-angle fusion, the image brightness can be improved while ensuring the overall filtering of strong reflections, retaining the maximum phase information.

[0137] It is worth mentioning that merging the final reconstructed image with the structured light fringe image is the last step in generating the target three-dimensional image. In this process, we first carefully preprocess the final reconstructed image to ensure that its resolution, size and pixel arrangement accurately match the structured light fringe image, and then precisely align the reconstructed image with the structured light fringe image in space to ensure that the information in the two images can be accurately superimposed together. On this basis, we make full use of the encoded information in the structured light fringe image, complex image analysis and decoding algorithms, accurately identify the deformation features of these stripes, and then convert them into accurate three-dimensional depth data. Finally, by combining the rich detail information in the reconstructed image with the depth information extracted from the structured light fringe image, a target three-dimensional image containing the precise three-dimensional shape of the object and rich surface details is generated.

[0138] In an embodiment of the present application, a structured light stripe image is first obtained based on the surface of a highly reflective object, and a plurality of multi-frequency polarization stripe images at different angles are obtained based on the structured light stripe image, and information loss that may be caused by a single angle is avoided by collecting from multiple angles; then, the overexposed area of ​​each multi-frequency polarization stripe image is determined by a local gray value analysis method and an adaptive threshold, and a targeted analysis is performed on each local area of ​​the multi-frequency polarization stripe image, so that the tiny details in the image can be captured more keenly; then, a mask image of the multi-frequency polarization stripe image is constructed through the overexposed area, and a highlight image corresponding to the multi-frequency polarization stripe image is obtained, and the overexposed area is marked through the mask image region, accurately segment the highlight image of each multi-frequency stripe image; according to the highlight image, construct the corresponding Gaussian pyramid image sequence and Laplace pyramid image sequence. The Laplace pyramid image sequence can more accurately capture and describe the detail features of the image by layering the detail information of different scales; construct a fusion pyramid according to the pixel maximum method, fuse the Gaussian image sequence corresponding to different highlight images with the Laplace pyramid image sequence, and highlight the brighter area information in the highlight image to the greatest extent; finally, reconstruct through the fusion pyramid, and merge the final reconstructed image with the structured light stripe image to output the target three-dimensional image. This application can effectively solve the problem of difficulty in restoring when detail features are lost due to overexposure.

[0139] See also Figures 4 to 5 , shown is a schematic diagram of the structure of a monocular structured light three-dimensional imaging system based on multi-frequency polarization projection provided by an embodiment of the second aspect of the present invention.

[0140] It should be noted that the monocular structured light 3D imaging method based on multi-frequency polarization projection provided in the embodiment of the present application can be executed by a monocular structured light 3D imaging system based on multi-frequency polarization projection, or, or a control module in the system for executing the loading of the monocular structured light 3D imaging method based on multi-frequency polarization projection. In the embodiment of the present application, the monocular structured light 3D imaging method based on multi-frequency polarization projection is executed by a monocular structured light 3D imaging system based on multi-frequency polarization projection as an example to illustrate the monocular structured light 3D imaging method based on multi-frequency polarization projection provided in the embodiment of the present application.

[0141] The monocular structured light three-dimensional imaging system based on multi-frequency polarization projection in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a laptop computer, an ultra-mobile personal computer (UMPC), etc., and the non-mobile electronic device can be a server, a personal computer (PC), a television (TV), etc., which is not specifically limited in the embodiment of the present application.

[0142] The monocular structured light 3D imaging system based on multi-frequency polarization projection in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0143] The monocular structured light three-dimensional imaging system based on multi-frequency polarization projection provided in the embodiment of the present application can achieve Figures 1 to 3 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0144] In some embodiments of the present application, the above-mentioned monocular structured light three-dimensional imaging system based on multi-frequency polarization projection includes:

[0145] The structured light image acquisition module 210 is used to acquire a structured light stripe image.

[0146] The multi-frequency polarization fringe image generation module 220 is used to obtain a plurality of multi-frequency polarization fringe images at different angles according to the structured light fringe image.

[0147] The pyramid image sequence construction module 230 is used to construct a Gaussian pyramid image sequence and a Laplacian pyramid image sequence corresponding to a single highlight image.

[0148] In this embodiment, the pyramid image sequence construction module further includes the following subunits:

[0149] The over-exposed region determining unit 231 is used to determine the over-exposed region of each multi-frequency polarization fringe image by using a local gray value analysis method.

[0150] The mask image generating unit 232 is used to construct a mask image of the multi-frequency polarization fringe image based on the overexposed area.

[0151] The highlight image acquisition unit 233 is used to acquire a corresponding highlight image according to the multi-frequency polarization fringe image and the mask image.

[0152] The Gaussian pyramid image sequence generating unit 234 is used to construct a corresponding Gaussian pyramid image sequence according to the highlight image.

[0153] The Laplacian pyramid image generating unit 235 is used to construct a corresponding Laplacian pyramid image sequence based on the Gaussian pyramid image sequence.

[0154] The fused pyramid image construction module 240 is used to construct a fused pyramid image based on multiple Gaussian pyramid image sequences and multiple Laplacian pyramid image sequences; wherein the topmost image of the fused pyramid image is a fused image of the topmost images of the multiple Gaussian pyramid image sequences, and the second topmost to bottommost images of the fused pyramid image are fused images of corresponding layers of the multiple Laplacian image sequences.

[0155] The reconstructed image acquisition module 250 acquires a final reconstructed image according to the fused pyramid image.

[0156] The three-dimensional imaging module 260 is used to merge the final reconstructed image with the structured light fringe image to obtain a target three-dimensional image.

[0157] In an embodiment of the present application, a structured light stripe image is first obtained based on the surface of a highly reflective object, and a plurality of multi-frequency polarization stripe images at different angles are obtained based on the structured light stripe image, and information loss that may be caused by a single angle is avoided by collecting from multiple angles; then, the overexposed area of ​​each multi-frequency polarization stripe image is determined by a local gray value analysis method and an adaptive threshold, and a targeted analysis is performed on each local area of ​​the multi-frequency polarization stripe image, so that the tiny details in the image can be captured more keenly; then, a mask image of the multi-frequency polarization stripe image is constructed through the overexposed area, and a highlight image corresponding to the multi-frequency polarization stripe image is obtained, and the overexposed area is marked through the mask image region, accurately segment the highlight image of each multi-frequency stripe image; according to the highlight image, construct the corresponding Gaussian pyramid image sequence and Laplace pyramid image sequence. The Laplace pyramid image sequence can more accurately capture and describe the detail features of the image by layering the detail information of different scales; construct a fusion pyramid according to the pixel maximum method, fuse the Gaussian image sequence corresponding to different highlight images with the Laplace pyramid image sequence, and highlight the brighter area information in the highlight image to the greatest extent; finally, reconstruct through the fusion pyramid, and merge the final reconstructed image with the structured light stripe image to output the target three-dimensional image. This application can effectively solve the problem of difficulty in restoring when detail features are lost due to overexposure.

[0158] See also Figure 6 , shown is an embodiment of the third aspect of the present application, providing an electronic device, including a processor 301, a memory 302, and a computer program 303 stored in the memory and executable on the processor. When the computer program 303 is executed by the processor 301, each process of the embodiment of the monocular structured light three-dimensional imaging method based on multi-frequency polarization projection is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0159] An embodiment of the fourth aspect of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned monocular structured light three-dimensional imaging method embodiment based on multi-frequency polarization projection are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0160] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0161] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0163] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A monocular structured light three-dimensional imaging method based on multi-frequency polarization projection, characterized in that: The method comprises: Acquire structured light stripe images based on the surface of highly reflective objects; Acquire a plurality of multi-frequency polarization fringe images at different angles according to the structured light fringe image; Determine the overexposed area of ​​each of the multi-frequency polarization fringe images by a local gray value analysis method; constructing a mask image of the multi-frequency polarization fringe image based on the overexposed area; Acquire a corresponding highlight image according to the multi-frequency polarization fringe image and the mask image; According to the highlight image, construct a corresponding Gaussian pyramid image sequence; Based on the Gaussian pyramid image sequence, construct a corresponding Laplacian pyramid image sequence; Based on the multiple Gaussian pyramid image sequences and the multiple Laplacian pyramid image sequences, a fused pyramid image is constructed; wherein the topmost image of the fused pyramid image is a fused image of the topmost images of the multiple Gaussian pyramid image sequences, and the second topmost to the bottommost images of the fused pyramid image are fused images of corresponding layers of the multiple Laplacian pyramid image sequences; According to the pixel maximum value fusion principle, the top-most images of the plurality of Gaussian pyramid image sequences are fused as the top-most image of the fused pyramid image, and the corresponding-layer images of the plurality of Laplacian pyramid image sequences are fused as the second-top to the bottom-most images of the fused pyramid image; The calculation method for fusing the top-most images of the plurality of Gaussian pyramid image sequences is as follows: F K =fuse(G 1K ，G 2K ，......，G nK ) ， in, K represents the top layer of the pyramid image sequence, n represents the number of Gaussian pyramid image sequences, n=1,2,3,......,n, G 1K 、G 2K 、G nK Respectively represent the topmost image of each Gaussian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F K represents the top-most fused image; The calculation method of the image fusion of the corresponding layers of the plurality of Laplacian image sequences is: F k =fuse(L 1k ，L 2k ，......，L nk )， in, k represents the number of layers, n represents the number of Laplacian pyramid image sequences, n=1,2,3,......,n, L 1k 、L 2k 、 L nk Respectively represent the kth layer of each Laplacian pyramid image sequence, fuse Indicates that the images are fused according to the pixel maximum fusion principle. F k represents the fused image of the kth layer; Obtaining a final reconstructed image according to the fused pyramid image; The final reconstructed image is combined with the structured light fringe image to obtain a target three-dimensional image.

2. The monocular structured light three-dimensional imaging method based on multi-frequency polarization projection according to claim 1, characterized in that: The step of determining the overexposed area of ​​each of the multi-frequency polarization fringe images by the local gray value analysis method specifically includes: According to a local gray value analysis method, the multi-frequency polarization stripe image is divided into a plurality of regional images; Calculate the average gray value and adaptive threshold of each area image respectively; If the average grayscale value is greater than the adaptive threshold, the image of the region is determined to be the overexposed region; The adaptive threshold is calculated as follows: T(x,y)=μ(x,y)-kσ(x,y), Where (x, y) represents the position of the regional image, T(x, y) represents the adaptive threshold of the regional image, μ(x, y) represents the average grayscale value of the adjacent regional image, σ(x, y) represents the standard deviation of the pixels of the adjacent regional image, and k represents the adjustment parameter.

3. The monocular structured light 3D imaging method based on multi-frequency polarization projection according to claim 1, characterized in that: The step of constructing the mask image of the multi-frequency polarization fringe image based on the overexposed area specifically includes: Comparing the overexposed area of ​​each of the multi-frequency polarization stripe images, and selecting the maximum overexposed multi-frequency polarization stripe image, wherein the maximum overexposed multi-frequency polarization stripe image represents the multi-frequency polarization stripe image with the largest overexposed area; Constructing the mask image having the same size as the maximum overexposed multi-frequency polarization fringe image; In the mask image, the pixel values ​​at the image positions of the overexposed regions are set to 1, and the pixel values ​​at the image positions of the non-overexposed regions are set to 0.

4. The monocular structured light 3D imaging method based on multi-frequency polarization projection according to claim 1, characterized in that: The step of constructing a corresponding Gaussian pyramid image sequence according to the highlight image specifically includes: Setting the highlight image at the bottom layer of the Gaussian pyramid image sequence; Downsampling the highlight image multiple times to obtain an image sequence with gradually decreasing resolution, and constructing the Gaussian pyramid image sequence corresponding to the highlight image from bottom to top; The number of layers of the Gaussian pyramid image sequence corresponding to each of the highlight images is the same.

5. The monocular structured light 3D imaging method based on multi-frequency polarization projection according to claim 1, characterized in that: The step of constructing a corresponding Laplacian pyramid image sequence based on the Gaussian pyramid image sequence specifically includes: Based on the Gaussian pyramid image sequence, construct the Laplacian pyramid image sequence starting from the bottom layer; The calculation method for constructing the Laplacian pyramid image sequence is: L k =G k -expand(G (k+1) )， in, k Indicates the number of layers, G k represents the image at the kth layer of the Gaussian pyramid image sequence, G (k+1) represents the image of the (k+1)th layer of the Gaussian pyramid image sequence, expand Indicates interpolation operation. L k Represents the image at the kth level of the Laplacian pyramid image sequence.

6. The monocular structured light 3D imaging method based on multi-frequency polarization projection according to claim 1, characterized in that: The step of obtaining the final reconstructed image according to the fused pyramid image specifically includes: Performing an upsampling operation on the topmost layer of the fused pyramid image, and adding the upsampling operation to the fused image at the next top layer, so as to obtain a reconstructed image at the next top layer of the fused pyramid image; Starting from the second top layer of the fusion pyramid, obtaining a reconstructed image of the current layer according to the reconstructed image of the previous layer and the fused image of the current layer; The reconstructed image is repeatedly acquired until the bottom layer of the fused pyramid image, and a final reconstructed image is output.

7. A monocular structured light 3D imaging system based on multi-frequency polarization projection, used to implement the steps of the monocular structured light 3D imaging method based on multi-frequency polarization projection as claimed in any one of claims 1 to 6, characterized in that: The system comprises: A structured light image acquisition module, used to acquire a structured light stripe image; A multi-frequency polarization stripe image generation module, used for acquiring a plurality of multi-frequency polarization stripe images at different angles according to the structured light stripe image; a pyramid image sequence construction module, configured to determine an overexposed area of ​​each of the multi-frequency polarization stripe images by a local gray value analysis method, construct a mask image of the multi-frequency polarization stripe image based on the overexposed area, obtain a corresponding highlight image according to the multi-frequency polarization stripe image and the mask image, construct a corresponding Gaussian pyramid image sequence according to the highlight image, and construct a corresponding Laplacian pyramid image sequence based on the Gaussian pyramid image sequence; A fused pyramid image construction module is used to construct a fused pyramid image based on the multiple Gaussian pyramid image sequences and the multiple Laplacian pyramid image sequences; wherein the topmost image of the fused pyramid image is a fused image of the topmost images of the multiple Gaussian pyramid image sequences, and the second topmost to bottommost images of the fused pyramid image are fused images of corresponding layers of the multiple Laplacian image sequences; A reconstructed image acquisition module, which acquires a final reconstructed image according to the fused pyramid image; The three-dimensional imaging module is used to merge the final reconstructed image with the original structured light image to obtain a target three-dimensional image.

8. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the monocular structured light three-dimensional imaging method based on multi-frequency polarization projection as described in any one of claims 1 to 6 are implemented.

9. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the monocular structured light three-dimensional imaging method based on multi-frequency polarization projection as described in any one of claims 1 to 6 are implemented.

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