A method, system, medium, and equipment for measuring the surface shape of a freeform surface mirror.

Abstract

This application discloses a method, system, medium, and device for measuring the surface shape of a freeform surface mirror, relating to the field of optical precision measurement technology. It aims to solve the problem of poor accuracy in measuring the surface shape of freeform surface mirrors for head-up displays in existing technologies. This application first acquires fringe images reflected by the primary and secondary mirrors to obtain an absolute phase map for ray tracing. Next, it uses stereo matching of corresponding points to calculate initial depth data to eliminate ambiguity in normal-height coupling. Then, it constructs a variational fusion model for solving. The model introduces a depth fidelity term to suppress low-frequency surface distortion caused by the traditional gradient integral method, while introducing a gradient constraint term to smooth quantization noise in binocular vision, preserving the mirror's subtle features. Finally, by solving the model, high-precision three-dimensional surface shape data of the primary and secondary mirrors are obtained, improving the measurement accuracy of the surface shape.

Description

Technical Field

[0001] This application relates to the field of optical precision measurement technology, specifically to a method, system, medium, and device for measuring the surface shape of a freeform surface mirror. Background Technology

[0002] Automotive head-up display (HUD) systems typically consist of a concave primary mirror and a convex secondary mirror, with their optical surfaces often being off-axis aspherical or freeform surfaces. HUD image quality is extremely sensitive to the surface accuracy of these mirrors. Existing measurement methods mainly include coordinate measuring machines (CMMs), interferometers, and phase deflection (PMD). CMMs are slow and prone to scratching surfaces; interferometers are difficult to measure large-curvature freeform surfaces and have limited dynamic range; traditional monocular deflection, while highly sensitive, suffers from "height-normal" coupling ambiguity and typically uses integral methods for reconstruction, easily leading to low-frequency cumulative errors. Traditional binocular vision, while capable of obtaining absolute depth, is limited by phase noise, resulting in poor high-frequency details in the reconstructed surface, failing to meet the optical-grade smoothness requirements of HUDs. Therefore, a more effective method for measuring surface accuracy is urgently needed. Summary of the Invention

[0003] In summary, the main objective of this application is to provide a method, system, medium, and device for measuring the surface shape of a freeform surface mirror, aiming to solve the problem of poor accuracy in measuring the surface shape of a freeform surface mirror for a head-up display in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In a first aspect, embodiments of this application provide a method for measuring the surface shape of a freeform surface reflector, applied to a head-up display, comprising the following steps:

[0006] The absolute phase map is obtained by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups.

[0007] The initial depth map is obtained by matching corresponding points based on the absolute phase map.

[0008] A variational fusion model is constructed based on the gradient field of the surface under test and the initial depth map; wherein, the gradient field of the surface under test is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model.

[0009] The three-dimensional surface shapes of the primary mirror and the secondary mirror are obtained by solving the problem based on the variational fusion model.

[0010] In one possible implementation of the first aspect, before constructing the variational fusion model based on the gradient field and initial depth map of the surface to be tested, the method further includes:

[0011] Based on the currently estimated surface height, camera optical center, and screen pixel coordinates, the gradient field of the surface under test is obtained by reverse tracing calculation using the law of reflection; wherein, the screen is used to project fringe images onto the primary and secondary mirrors of the target head-up display.

[0012] In one possible implementation of the first aspect, the gradient field of the surface to be measured is obtained by reverse tracing calculation using the law of reflection, based on the currently estimated surface height, camera optical center, and screen pixel coordinates, including:

[0013] Based on the camera calibration parameters, obtain the camera optical center and line-of-sight vector;

[0014] Based on the currently estimated surface height, obtain the intersection point of the line-of-sight vector and the surface to be measured;

[0015] Based on the mapping relationship represented by the absolute phase map, the screen physical coordinates corresponding to the intersection point are obtained;

[0016] Based on the screen's physical coordinates, intersection points, and the camera's optical center, construct the incident ray vector and the observation vector;

[0017] Based on the incident ray vector and the observation vector, the surface normal vector is calculated to obtain the gradient field of the surface under test.

[0018] In one possible implementation of the first aspect, an initial depth map is obtained by matching corresponding points based on the absolute phase map, including:

[0019] Based on the principles of epipolar constraint and phase consistency, corresponding points are matched on the absolute phase map to obtain corresponding points.

[0020] The spatial coordinates of corresponding points are calculated based on the principle of triangulation to obtain an initial depth map.

[0021] In one possible implementation of the first aspect, an absolute phase map is obtained by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, respectively, including:

[0022] By acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, a mapping relationship is constructed between the camera pixels and the pixels of the screen projecting the fringe images, thus obtaining an absolute phase map.

[0023] In one possible implementation of the first aspect, before obtaining the absolute phase map by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display based on two binocular camera groups respectively, the method further includes:

[0024] Based on the requirement that the camera's field of view covers the primary and secondary mirrors of the target head-up display, a binocular camera group is constructed for each.

[0025] In one possible implementation of the first aspect, after obtaining the three-dimensional surface shapes of the primary mirror and the secondary mirror based on the variational fusion model, the method further includes:

[0026] The three-dimensional surface shapes of the primary mirror and the secondary mirror are fused into the same design coordinate system to evaluate the assembly error of the optical path system of the target head-up display.

[0027] Secondly, embodiments of this application provide a freeform surface reflector shape measurement system, applied to a head-up display, comprising:

[0028] The acquisition module is used to acquire fringe images reflected by the primary and secondary mirrors of the target head-up display based on two binocular camera groups, respectively, to obtain an absolute phase map;

[0029] The matching module is used to perform corresponding point matching based on the absolute phase map to obtain the initial depth map;

[0030] The construction module is used to construct a variational fusion model based on the gradient field and initial depth map of the surface under test; wherein, the gradient field of the surface under test is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model.

[0031] The solver module is used to solve the problem based on the variational fusion model to obtain the three-dimensional surface shapes of the primary mirror and the secondary mirror.

[0032] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the freeform surface mirror shape measurement method provided in any of the first aspects above.

[0033] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein,

[0034] Memory is used to store computer programs;

[0035] The processor is used to load and execute a computer program to cause the electronic device to perform the freeform surface mirror shape measurement method provided in any of the first aspects above.

[0036] Compared with the prior art, the beneficial effects of this application are:

[0037] This application proposes a method, system, medium, and device for measuring the surface shape of a freeform surface mirror. The method includes: acquiring fringe images of the primary and secondary mirrors of a target head-up display based on two binocular camera groups to obtain an absolute phase map; performing corresponding point matching based on the absolute phase map to obtain an initial depth map; constructing a variational fusion model based on the gradient field of the surface to be measured and the initial depth map; wherein the gradient field of the surface to be measured is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model; and solving the variational fusion model to obtain the three-dimensional surface shape of the primary mirror and the three-dimensional surface shape of the secondary mirror. This application first acquires fringe images reflected by the primary and secondary mirrors to obtain an absolute phase map for ray tracing. Then, it uses stereo matching of corresponding points to calculate the initial depth data to eliminate the ambiguity of normal-height coupling. Next, it constructs a variational fusion model for solving. The model introduces a depth fidelity term to suppress the low-frequency surface distortion caused by the traditional gradient integral method, while introducing a gradient constraint term to smooth the quantization noise in binocular vision and preserve the small features of the mirror surface. Finally, by solving the model, high-precision three-dimensional surface data of the primary and secondary mirrors are obtained, improving the measurement effect of surface accuracy. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application;

[0039] Figure 2 A flowchart illustrating the method for measuring the surface shape of a freeform surface mirror provided in this application embodiment;

[0040] Figure 3 A schematic diagram illustrating the application scenario of the freeform surface mirror shape measurement method provided in this application embodiment;

[0041] Figure 4 This is a schematic diagram illustrating the coupling relationship between height and normal in monocular deflection surgery.

[0042] Figure 5 A flowchart illustrating one implementation of the freeform surface mirror shape measurement method provided in this application embodiment;

[0043] Figure 6 A schematic diagram of the residuals between the freeform surface mirror shape measurement method provided in this application embodiment and the existing reconstruction algorithm;

[0044] Figure 7 A schematic diagram of the module of the freeform surface mirror shape measurement system provided in the embodiments of this application;

[0045] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0046] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0047] See attached document Figure 1 , attached Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0048] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0049] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a freeform surface mirror shape measurement system.

[0050] In the appendix Figure 1In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the freeform surface mirror shape measurement system stored in the memory 105 through the processor 101 and executes the freeform surface mirror shape measurement method provided in the embodiment of this application.

[0051] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide a method for measuring the surface shape of a freeform surface reflector, applied to a head-up display, including the following steps:

[0052] S10: Based on two binocular camera groups, the stripe images reflected by the primary and secondary mirrors of the target head-up display are acquired respectively to obtain the absolute phase map.

[0053] In the specific implementation process, the target head-up display (HUD) is the HUD to be measured. A sinusoidal fringe image is projected onto the screen, and the primary and secondary mirrors reflect this fringe image. The primary and secondary mirrors are simultaneously acquired by a binocular camera group to obtain an absolute phase map, which provides a basis for subsequent ray tracing. (See attached...) Figure 3 The application scenario shown includes a display screen and four industrial cameras. The four-camera architecture can handle the large curvature measurement requirements of the primary and secondary mirrors of the HUD, and narrow-band filters or polarizers are mounted in front of the camera lenses to filter out ambient stray light. The display screen uses a high-resolution LCD or OLED display, and its surface is coated with a diffuse reflection film to eliminate the interference of specular reflection on the measurement. Cameras 1 and 2 form the first binocular unit, i.e., a binocular camera group, to cover the field of view of the primary mirror. Cameras 3 and 4 form the second binocular unit, i.e., another binocular camera group, to cover the field of view of the secondary mirror. That is, before obtaining the absolute phase map by acquiring the fringe images reflected by the primary and secondary mirrors of the target HUD based on the two binocular camera groups respectively, the method also includes:

[0054] Based on the requirement that the camera's field of view covers the primary and secondary mirrors of the target head-up display, a binocular camera group is constructed for each.

[0055] In one embodiment, an absolute phase map is obtained by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, respectively, including:

[0056] By acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, a mapping relationship is constructed between the camera pixels and the pixels of the screen projecting the fringe images, thus obtaining an absolute phase map.

[0057] In practical implementation, the absolute phase map is used to characterize the mapping relationship from camera pixels to screen pixels. Based on phase deflection, the N-step phase shift method is used to calculate the wrapped phase, and the time phase is unwrapped using the multi-frequency heterodyne method to obtain the absolute phase map Φ(u, v). The N-step phase shift method refers to sequentially projecting N (usually N≥3) sinusoidal fringe images with a phase increment of 2π / N, and solving the light intensity change of each pixel by the least squares method, which can calculate the high-precision phase value point by point. Compared with binary encoding, the phase shift method has extremely strong robustness to ambient light non-uniformity and surface reflectivity changes.

[0058] S20: Perform corresponding point matching based on the absolute phase map to obtain the initial depth map.

[0059] In practical implementation, corresponding point matching is used to achieve stereo matching between binocular cameras. Corresponding points are found on the absolute phase maps of the two binocular camera groups to obtain a reliable initial depth value for subsequent calculations. Specifically, binocular phase matching is used to calculate an unambiguous coarse depth as a low-frequency reference to eliminate normal-height ambiguity. Specifically, corresponding point matching is performed based on the absolute phase map to obtain an initial depth map, including:

[0060] Based on the principles of epipolar constraint and phase consistency, corresponding points are matched on the absolute phase map to obtain corresponding points.

[0061] The spatial coordinates of corresponding points are calculated based on the principle of triangulation to obtain an initial depth map.

[0062] In practical implementation, epipolar constraint means that for any point in space, the image point on the left camera image must have a corresponding image point on the right camera image plane located on a specific straight line called the epipolar line. This constraint reduces the search range of corresponding points from a two-dimensional full image to a one-dimensional straight line, significantly improving the efficiency and accuracy of stereo matching. Phase consistency refers to the location where meaningful features (such as edges) in an image achieve maximum phase consistency in the Fourier components of the image locally. In other words, at edges, the image signal has the same phase across multiple frequency scales. Through corresponding point matching, once the corresponding point is found and combined with camera parameters, the three-dimensional spatial position of that point can be calculated using triangulation, thus obtaining the initial depth data of the surface to be measured. Although the data obtained at this time contains high-frequency noise, its low-frequency geometry is confirmed, and there is no height-normal ambiguity problem in monocular deflection. The coupling relationship between height and normal in monocular deflection is shown in the attached figure. Figure 4 As shown, different height assumptions result in different calculated gradients, i.e., normals.

[0063] S30: Based on the gradient field and initial depth map of the surface under test, construct a variational fusion model; wherein, the gradient field of the surface under test is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model.

[0064] In practical implementation, the core of constructing a variational fusion model is to construct a global energy functional. Finding the optimal surface This minimizes energy. The functional simultaneously includes a depth-fidelity term that forces the reconstructed surface to approximate the initial depth data, and a gradient constraint term that forces the reconstructed surface gradient to conform to the calculated gradient field. That is, it simultaneously approximates both binocular depth and monocular gradient, suppressing low-frequency surface distortion caused by traditional gradient integration methods and smoothing quantization noise in binocular vision using high-precision gradient field constraints, while preserving the subtle features of the HUD mirrors. Global Energy Function The mathematical expression is as follows:

[0065]

[0066] in, This represents the initial depth map, i.e., the initial depth data, and Ω represents the reconstruction region. = , where is the mathematical gradient of the surface to be determined. , These are the depth constraint weights and gradient constraint weights, used to balance the confidence levels of the two types of data. The correlation coefficient with stereo matching is positively correlated. The weights are positively correlated with the modulation of the acquired stripe image. In subsequent iterative optimization, the weights are dynamically adjusted based on the residuals to suppress high-frequency noise and local reflection interference.

[0067] The gradient field can be obtained by inverse calculation based on the physical optics reflection model. That is, before constructing the variational fusion model based on the gradient field of the surface to be measured and the initial depth map, the method also includes:

[0068] Based on the currently estimated surface height, camera optical center, and screen pixel coordinates, the gradient field of the surface under test is obtained by reverse tracing calculation using the law of reflection; wherein, the screen is used to project fringe images onto the primary and secondary mirrors of the target head-up display.

[0069] Specifically: Based on the currently estimated surface height, camera optical center, and screen pixel coordinates, the gradient field of the surface under test is obtained by reverse tracing using the law of reflection, including:

[0070] Based on the camera calibration parameters, obtain the camera optical center and line-of-sight vector;

[0071] Based on the currently estimated surface height, obtain the intersection point of the line-of-sight vector and the surface to be measured;

[0072] Based on the mapping relationship represented by the absolute phase map, the screen physical coordinates corresponding to the intersection point are obtained;

[0073] Based on the screen's physical coordinates, intersection points, and the camera's optical center, construct the incident ray vector and the observation vector;

[0074] Based on the incident ray vector and the observation vector, the surface normal vector is calculated to obtain the gradient field of the surface under test.

[0075] In the specific implementation process, the law of reflection is used for reverse tracking calculation. That is, for any pixel on the image, its optical center position O and normalized line-of-sight vector v are determined according to the camera calibration parameters. Based on the currently estimated height, the intersection point P of the line of sight and the curved surface is calculated. The screen physical coordinates S corresponding to this point are retrieved according to the absolute phase map, and the incident ray vector l=S is constructed. P and observation vector o=O P, surface normal vector n = (n x ,n y ,n z Let n be the angle bisector of l, and n = (l / ‖l‖ + o / ‖o‖). Convert this to the gradient value G = ( n x / n z , n y / n z） , represents the slope vector of the surface to be measured at pixel (x, y), n x n y n z These represent the components of the normal vector in the horizontal (X-axis), vertical (Y-axis), and depth (Z-axis) directions, respectively.

[0076] S40: Solve based on the variational fusion model to obtain the three-dimensional surface shapes of the primary mirror and the secondary mirror.

[0077] In practical implementation, to solve for the extrema of the functional, the variational principle is applied. A necessary condition for the functional to be minimal is that its first variation is zero, i.e. The corresponding Euler-Lagrange equations are derived, and a sparse linear system of equations is constructed using the finite difference method for iterative numerical solution, thereby obtaining a high-precision 3D surface shape. Specifically:

[0078] First, according to Green's theorem, the variation of the squared term of the gradient modulus corresponds to the Laplace operator (or divergence operator). Through derivation, the corresponding Euler-Lagrange equation is obtained:

[0079]

[0080] Where E is the global energy functional, Z is the surface height function to be solved, and Z0 is the surface height function to be solved. x、 Z y Let x be the partial derivative of the surface height function with respect to x and y. , It is a differential operator.

[0081] Substituting the specific energy terms, we obtain the following Poisson-type partial differential equation:

[0082]

[0083] Expanding the divergence term further, the equation can be rewritten as:

[0084]

[0085] Then, finite difference discretization: To solve this continuous equation on a computer, it needs to be spatially discretized. At pixel grid (i,j), the Laplace operator is approximated using a five-point difference scheme. :

[0086]

[0087] in, Here, is the divergence term, describing the local flux change of the weighted gradient field, and h is the pixel grid step size, representing the physical distance between adjacent sampling points. This represents the height of the current pixel carrying the ball. This represents the height of the current point relative to its horizontal neighbors, with ± indicating different sides of the current point. These are the weighting coefficients, determined by the gradient constraint weights. The mean value between adjacent pixels determines the result.

[0088] This will generate a large sparse linear system of equations: AZ=b, where A is a pentagonal sparse matrix containing weight coefficients; Z is the height vector to be determined; and b is the source term vector containing the binocular depth and the current gradient divergence.

[0089] Finally, a fixed-point iteration solution strategy is adopted: Since G(Z) on the right-hand side of the equation is nonlinearly dependent on the unknown Z, a fixed-point iteration method is used.

[0090] (1) Initialize Z (0) =Z stereo ;

[0091] (2) Enter the iteration loop (k=0,1,...);

[0092] (3) Based on the current height Z (k) Update the gradient field G using the deflection model (k) (Correct gradient direction);

[0093] (4) Constructing the linear equation system AZ (k+1) =b(G (k) );

[0094] (5) Solve the large sparse equation system using the preprocessed conjugate gradient method to obtain the updated height Z. (k+1) ;

[0095] (6) Determine the convergence condition , If the set threshold is met, the result is output; otherwise, return to step (3).

[0096] In this embodiment, firstly, absolute phase maps are obtained by acquiring fringe images reflected by the primary and secondary mirrors respectively for ray tracing. Secondly, stereo matching of corresponding points is used to calculate the initial depth data to eliminate the ambiguity of normal-height coupling. Then, a variational fusion model is constructed for solving. The model introduces a depth fidelity term to suppress the low-frequency surface distortion generated by the traditional gradient integral method, while introducing a gradient constraint term to smooth the quantization noise in binocular vision and preserve the small features of the mirror surface. Finally, by solving the model, high-precision three-dimensional surface data of the primary and secondary mirrors are obtained, improving the measurement effect of surface accuracy.

[0097] In one embodiment, after obtaining the three-dimensional surface shapes of the primary mirror and the secondary mirror based on the variational fusion model, the method further includes:

[0098] The three-dimensional surface shapes of the primary mirror and the secondary mirror are fused into the same design coordinate system to evaluate the assembly error of the optical path system of the target head-up display.

[0099] In the specific implementation process, after outputting the reconstructed three-dimensional surface shapes of the primary mirror and the secondary mirror, in order to further measure the local slope error, the data of the primary mirror and the secondary mirror are fused into the same design coordinate system to evaluate the assembly error of the HUD optical path system and effectively guide the implementation of imaging quality improvement measures.

[0100] See attached document Figure 5 In the attached Figure 5 The present application will be further described below with reference to the embodiments shown:

[0101] First, image acquisition and phase unwrapping are performed. Second, a coarse depth is obtained through binocular stereo matching, and a high-sensitivity gradient is obtained through gradient inverse calculation. Then, a variational energy functional is constructed using the above two methods. Finally, the solution is obtained through iterative optimization loops, outputting a high-precision 3D surface shape. The method provided in this application is compared with existing reconstruction algorithms; a residual diagram is attached. Figure 6As shown, (a) is the traditional monocular integration method, which has low-frequency surface distortion; (b) is the traditional binocular vision method, which has high-frequency random noise; and (c) is the variational fusion method provided in the embodiments of this application. The reconstructed HUD surface is accurate and smooth. It is superior to the traditional monocular integration method (no warping) in absolute position accuracy and superior to the traditional binocular vision method (no noise) in surface smoothness and slope error. It can meet the detection requirements of HUD imaging quality.

[0102] See attached document Figure 7 Based on the same inventive concept as in the foregoing embodiments, this application also provides a freeform surface reflector shape measurement system for use in a head-up display, comprising:

[0103] The acquisition module is used to acquire fringe images reflected by the primary and secondary mirrors of the target head-up display based on two binocular camera groups, respectively, to obtain an absolute phase map;

[0104] The matching module is used to perform corresponding point matching based on the absolute phase map to obtain the initial depth map;

[0105] The construction module is used to construct a variational fusion model based on the gradient field and initial depth map of the surface under test; wherein, the gradient field of the surface under test is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model.

[0106] The solver module is used to solve the problem based on the variational fusion model to obtain the three-dimensional surface shapes of the primary mirror and the secondary mirror.

[0107] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated onto one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the freeform surface mirror shape measurement system in this embodiment corresponds one-to-one with each step in the freeform surface mirror shape measurement method in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned freeform surface mirror shape measurement method, which will not be repeated here.

[0108] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the freeform surface reflector shape measurement method provided in the embodiments of this application.

[0109] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein,

[0110] Memory is used to store computer programs;

[0111] The processor is used to load and execute computer programs to enable electronic devices to perform the freeform surface mirror shape measurement method provided in the embodiments of this application.

[0112] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0113] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0114] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0115] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0116] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0117] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0119] In summary, this application provides a method, system, medium, and device for measuring the surface shape of a freeform surface mirror. The method includes: acquiring fringe images of the primary and secondary mirrors of a target head-up display based on two binocular camera groups to obtain an absolute phase map; performing corresponding point matching based on the absolute phase map to obtain an initial depth map; constructing a variational fusion model based on the gradient field of the surface to be measured and the initial depth map; wherein the gradient field of the surface to be measured is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model; and solving the variational fusion model to obtain the three-dimensional surface shape of the primary mirror and the three-dimensional surface shape of the secondary mirror. This application first acquires fringe images reflected by the primary and secondary mirrors to obtain an absolute phase map for ray tracing. Then, it uses stereo matching of corresponding points to calculate the initial depth data to eliminate the ambiguity of normal-height coupling. Next, it constructs a variational fusion model for solving. The model introduces a depth fidelity term to suppress the low-frequency surface distortion caused by the traditional gradient integral method, while introducing a gradient constraint term to smooth the quantization noise in binocular vision and preserve the small features of the mirror surface. Finally, by solving the model, high-precision three-dimensional surface data of the primary and secondary mirrors are obtained, improving the measurement effect of surface accuracy.

[0120] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for measuring the surface shape of a freeform surface reflecting mirror, characterized in that, When applied to a head-up display, the following steps are included: The absolute phase map is obtained by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups. Based on the absolute phase map, corresponding points are matched to obtain an initial depth map; Based on the currently estimated surface height, camera optical center, and screen pixel coordinates, the gradient field of the surface under test is obtained by reverse tracing calculation using the law of reflection; wherein, the screen is used to project the stripe image onto the primary and secondary mirrors of the target head-up display; based on the gradient field of the surface under test and the initial depth map, a variational fusion model is constructed; wherein, the gradient field of the surface under test is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model; The three-dimensional surface shapes of the primary mirror and the secondary mirror are obtained by solving the variational fusion model.

2. The method for measuring the surface shape of a freeform surface reflecting mirror according to claim 1, characterized in that, The gradient field of the surface under test is obtained by reverse tracing calculation using the law of reflection, based on the currently estimated surface height, camera optical center, and screen pixel coordinates, including: Based on the camera calibration parameters, obtain the camera optical center and line-of-sight vector; Based on the currently estimated surface height, the intersection point of the line-of-sight vector and the surface to be measured is obtained; Based on the mapping relationship represented by the absolute phase map, the screen physical coordinates corresponding to the intersection point are obtained; Based on the screen physical coordinates, the intersection point, and the camera optical center, construct the incident ray vector and the observation vector; Based on the incident ray vector and the observation vector, the surface normal vector is calculated to obtain the gradient field of the surface under test.

3. The method for measuring the surface shape of a freeform surface mirror according to claim 1, characterized in that, The step of performing corresponding point matching based on the absolute phase map to obtain an initial depth map includes: Based on the principles of epipolar constraint and phase consistency, corresponding points are matched on the absolute phase map to obtain corresponding points. The spatial coordinates of the corresponding points are calculated based on the principle of triangulation to obtain the initial depth map.

4. The method for measuring the surface shape of a freeform surface reflecting mirror according to claim 1, characterized in that, The process of acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups to obtain an absolute phase map includes: By acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, a mapping relationship is constructed between the camera pixels and the pixels of the screen projecting the fringe images, thus obtaining an absolute phase map.

5. The method for measuring the surface shape of a freeform surface mirror according to claim 1, characterized in that, Before obtaining the absolute phase map by acquiring fringe images reflected from the primary and secondary mirrors of the target head-up display using two binocular camera groups, the method further includes: Based on the requirement that the camera's field of view covers the primary and secondary mirrors of the target head-up display, a binocular camera group is constructed for each.

6. The method for measuring the surface shape of a freeform surface reflecting mirror according to claim 1, characterized in that, After obtaining the three-dimensional surface shapes of the primary mirror and the secondary mirror by solving the variational fusion model, the method further includes: The three-dimensional surface shapes of the primary mirror and the secondary mirror are fused into the same design coordinate system to evaluate the assembly error of the optical path system of the target head-up display.

7. A system for measuring the surface shape of a freeform surface mirror, characterized in that, For implementing the method as described in claim 1, applied to a head-up display, comprising: The acquisition module is used to acquire fringe images reflected by the primary and secondary mirrors of the target head-up display based on two binocular camera groups, respectively, to obtain an absolute phase map; The matching module is used to perform corresponding point matching based on the absolute phase map to obtain an initial depth map; A construction module is used to construct a variational fusion model based on the gradient field of the surface to be tested and the initial depth map; wherein, the gradient field of the surface to be tested is used to construct the gradient constraint term of the variational fusion model, and the initial depth map is used to construct the depth fidelity term of the variational fusion model; The solution module is used to solve the variational fusion model to obtain the three-dimensional surface shapes of the primary mirror and the secondary mirror.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the freeform surface mirror shape measurement method as described in any one of claims 1-6.

9. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the freeform surface mirror shape measurement method as described in any one of claims 1-6.

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