Synthetic aperture radar microwave vision three-dimensional imaging method and device

Abstract

The present invention discloses a synthetic aperture radar microwave vision three-dimensional imaging method and device, and belongs to the field of synthetic aperture radar three-dimensional imaging. Based on the microwave vision information in the SAR image or the initial SAR three-dimensional point cloud, an iterative three-dimensional imaging method based on SAR microwave vision constraints is proposed; at the same time, in view of the fact that the calculation of each pixel point in sparse three-dimensional imaging is independent, a graphics processor is used to accelerate the three-dimensional imaging solution in parallel. Compared with the traditional method, after introducing microwave vision information, this method can achieve high-precision three-dimensional imaging under the condition of a small number of coherent observations, and further improve the imaging accuracy in iteration; at the same time, the use of a graphics processor reduces the time required for sparse three-dimensional imaging.

Description

Technical Field

[0001] The invention belongs to the field of synthetic aperture radar three-dimensional imaging, and in particular relates to a synthetic aperture radar microwave vision three-dimensional imaging method and device. Background Art

[0002] Synthetic Aperture Radar (SAR) 3D imaging uses multiple highly coherent observations to form a synthetic aperture, thereby obtaining the third-dimensional resolution capability. Compared with traditional interferometric SAR, SAR 3D imaging can separate overlapping targets and has great application potential and practical value in surveying and mapping, disaster prevention and mitigation, and other fields.

[0003] Common systems of three-dimensional SAR include multi-pass re-orbit tomography mode and single-pass array interferometry mode. Generally speaking, dozens of re-orbit tomography observations or dozens of antenna array elements are required to ensure the size and resolution of the third-dimensional synthetic aperture. In 2012, Zhu et al. proposed the SL1MMER sparse three-dimensional imaging algorithm based on compressed sensing theory, which effectively reduced the number of coherent observations required for SAR three-dimensional imaging, and can complete the three-dimensional reconstruction of the city with only a dozen coherent observations. However, due to the limitations of the number of commercial SAR satellites in orbit and orbit control capabilities, it still takes at least 2-3 months to obtain effective coherent data in the same area. The array interferometry mode requires more than a dozen antenna array elements, which makes its cost and system complexity relatively high. These problems have greatly limited the promotion and application of three-dimensional SAR. Summary of the invention

[0004] To solve the above technical problems, the present invention provides a synthetic aperture radar microwave vision three-dimensional imaging method and device, which fully utilizes the geometry, polarization and other microwave vision information in the SAR image or the initial SAR three-dimensional point cloud as constraints, and realizes high-precision three-dimensional imaging with a small number of observations in an iterative three-dimensional imaging framework.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A synthetic aperture radar microwave vision three-dimensional imaging method, the method comprising the following steps:

[0007] Step 1: collect M groups of coherent fully polarized echo data for the target scene, and perform two-dimensional SAR imaging on the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images;

[0008] Step 2, performing interferometric image registration on the M single-view complex images;

[0009] Step 3, extracting microwave visual information from the registered M single-view complex images;

[0010] Step 4: Establish an observation matrix pixel by pixel, and construct constraints of microwave vision information based on the extracted microwave vision information;

[0011] Step 5: Based on the OMP algorithm, parallel sparse 3D imaging is performed in combination with a graphics processor (GPU) to obtain a 3D point cloud of the target scene rendered in polarization pseudo-color.

[0012] Step 6: Determine whether the distance between the coordinates of each point in the 3D point cloud obtained in this round and the coordinates of each point in the 3D point cloud obtained in the previous round is less than the convergence threshold. If it is less than the convergence threshold, end the iteration and go to step 9; otherwise, go to step 7.

[0013] Step 7: extracting geometric semantic information belonging to different target structures from the three-dimensional point cloud rendered by polarization pseudo-color of the target scene obtained in step 5 by line and surface detection methods; and extracting polarization semantic information belonging to different target structures by using polarization pseudo-color information;

[0014] Step 8, mapping the affiliation relationship of different target structures extracted in step 7 to the two-dimensional single-view complex image, and returning to step 4;

[0015] Step 9: Screen and filter the iterated 3D point cloud to obtain the final 3D point cloud of the target scene.

[0016] In another aspect, the present invention provides a synthetic aperture radar microwave vision three-dimensional imaging device, comprising:

[0017] A data acquisition unit is used to collect M groups of coherent fully polarized echo data for the target scene, and complete two-dimensional SAR imaging of the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images;

[0018] A constraint construction unit is used to perform interferometric image registration on M single-view complex images; extract microwave visual information from the registered M single-view complex images; establish an observation matrix pixel by pixel, and construct constraints on the microwave visual information based on the extracted microwave visual information;

[0019] The final 3D point cloud construction unit is used to perform parallel sparse 3D imaging based on the OMP algorithm in combination with a graphics processor GPU to obtain a 3D point cloud of the target scene rendered with polarization pseudo-color, and to determine whether the distance between the coordinates of each point of the 3D point cloud obtained in this round and the coordinates of each point of the 3D point cloud obtained in the previous round is less than a convergence threshold. If it is less than the convergence threshold, the iteration is terminated to obtain the final 3D point cloud of the target scene; otherwise, from the 3D point cloud of the polarization pseudo-color rendered target scene obtained, geometric semantic information belonging to different target structures is extracted by line and surface detection methods, polarization semantic information belonging to different target structures is extracted using polarization pseudo-color information, the affiliation of the extracted different target structures is mapped to a 2D single-view complex image, and the constraints of the microwave visual information are reconstructed; the 3D point cloud that has completed the iteration is screened and filtered to obtain the final 3D point cloud of the target scene.

[0020] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned synthetic aperture radar microwave vision three-dimensional imaging method.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned synthetic aperture radar microwave vision three-dimensional imaging method.

[0022] The beneficial effects of the present invention are:

[0023] The present invention makes full use of the microwave visual information such as geometry and polarization in SAR images or initial SAR three-dimensional point clouds, and realizes high-precision three-dimensional imaging with a small number of observations in an iterative three-dimensional imaging framework, which significantly reduces the data acquisition cost of SAR three-dimensional imaging and is conducive to the promotion and application of SAR three-dimensional imaging technology. At the same time, the use of GPU enables large-scale sparse imaging tasks to be completed in a shorter time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of the synthetic aperture radar microwave vision three-dimensional imaging method of the present invention;

[0025] Figure 2 This is a real picture of the synthetic aperture radar microwave vision three-dimensional imaging system of the present invention;

[0026] Figure 3 is the laser point cloud image of the imaging area;

[0027] Figure 4Full polarization SAR images of the imaging area, where (A) represents HH polarization, (B) represents HV polarization, (C) represents VH polarization, and (D) represents VV polarization;

[0028] Figure 5 It is the semantic segmentation mask result map of the SAR image in the imaging area;

[0029] Figure 6 The figure is a comparison result diagram of the synthetic aperture radar microwave vision three-dimensional imaging method based on the present invention and the prior art method, wherein (A) represents the result diagram of the traditional method (unconstrained), and (B) represents the result diagram of the method of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0031] The present invention provides a synthetic aperture radar microwave vision three-dimensional imaging method, such as Figure 1 As shown, including:

[0032] Step 1: collect M groups of coherent fully polarized echo data for the target scene, and perform two-dimensional SAR imaging on the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images;

[0033] Step 2: Perform interferometric image registration on M single-view complex images:

[0034] Determine a main channel, and use the maximum spectrum method or existing mature interference image registration methods to register other channel images with the main channel image;

[0035] Step 3: Extract microwave visual information from the registered M single-view complex images:

[0036] In the main channel image, the geometric semantic information belonging to different target structures in the two-dimensional SAR image is extracted by line and surface detection methods; the Pauli pseudo-color image is generated by using the fully polarized single-view complex image, and the polarization semantic information belonging to different target structures is obtained from it according to the polarization scattering feature texture extraction; the polarization scattering features include neighborhood consistency, continuity, and intra-class correlation.

[0037] Step 4: Establish an observation matrix pixel by pixel, and construct constraints of microwave vision information based on the extracted microwave vision information:

[0038]

[0039] ;

[0040] in, Indicates the slant height of the scattering point, agrmin indicates the minimum value, The inversion residual of the scattering vector, The observation vector, The observation matrix, The pseudo-inverse of the measurement matrix, The mean is , the covariance matrix is The multivariate normal probability density function of represents the inversion error at the signal level, The mean is , the covariance matrix is The normal probability density function of represents the geometric semantic constraints. The mean is , the covariance matrix is The normal probability density function of represents the polarization semantic constraint, represents the backscattering coefficient;

[0041] Step 5: Based on the OMP algorithm, parallel sparse 3D imaging is performed in combination with a graphics processor (GPU) to obtain a 3D point cloud of the target scene rendered in polarization pseudo-color.

[0042] Step 6: Determine whether the distance between the coordinates of each point in the 3D point cloud obtained in this round and the coordinates of each point in the 3D point cloud obtained in the previous round is less than the convergence threshold. If it is less than the convergence threshold, end the iteration and go to step 9; otherwise, go to step 7.

[0043] Step 7: extracting geometric semantic information belonging to different target structures from the three-dimensional point cloud rendered by polarization pseudo-color of the target scene obtained in step 5 by line and surface detection methods; and extracting polarization semantic information belonging to different target structures by using polarization pseudo-color information;

[0044] Step 8, mapping the affiliation relationship of different target structures extracted in step 7 to the two-dimensional single-view complex image, and returning to step 4;

[0045] Step 9: Screen and filter the iterated 3D point cloud to obtain the final 3D point cloud of the target scene.

[0046] Preferably, step 5 comprises:

[0047] Step 5.1, data preparation and segmentation: divide the input observation data into multiple pixels, and perform 3D reconstruction for each pixel independently, and upload the divided observation data to the GPU memory;

[0048] Step 5.2: Initialization of the OMP algorithm: For each pixel, initialize the observation matrix , observation vector and the residual vector , the GPU operates on all pixels in parallel and allocates computing resources;

[0049] Step 5.3, iterative sparse signal solution: For each pixel, the OMP algorithm iteratively searches for the observation matrix Columns such that the current column is consistent with the residual vector The inner product of is the largest, including: calculating each candidate basis vector and the residual vector The correlation, that is, the projection calculation , select from all projections the same as the residual vector The most relevant basis vector, add it to the active set;

[0050] Step 5.4, residual update: For each iteration of the selected basis vector, the GPU updates the sparse solution by solving the linear equation , and recalculate the updated residual vector ;

[0051] Step 5.5, Iteration stop condition: After each pixel completes several iterations, the GPU checks the residual vector Whether it is lower than the set threshold, or whether the number of non-zero items of the sparse vector reaches the preset upper limit. If any stopping condition is met, the OMP algorithm terminates and the sparse solution of the corresponding pixel is obtained;

[0052] Step 5.6, 3D point cloud reconstruction: According to the sparse solution obtained by the OMP algorithm, the 3D coordinates of each pixel are reconstructed into a sparse 3D point cloud;

[0053] Step 5.7, polarization pseudo-color rendering: Process and render the illumination, texture, and polarization information of the sparse 3D point cloud data through the GPU to generate a polarization pseudo-color 3D point cloud;

[0054] Step 5.8, data transmission and result saving: The polarization pseudo-color 3D point cloud data calculated in the GPU is transmitted back to the host memory, and the result is saved as a 3D point cloud file.

[0055] Example

[0056] This embodiment uses a small drone-mounted array interferometric SAR system to collect 4-channel full-polarization array interferometric SAR data (M = 4) in a building area in Suzhou, Jiangsu Province. The actual photo of the array interferometric SAR system is shown in Figure 2. Figure 2 The main technical parameters are shown in Table 1. The laser point cloud data of the building area is used as the reference truth value of the final result, as shown in Figure 3 shown.

[0057] Table 1

[0058]

[0059] Using the method proposed in the present invention, firstly, the fully polarized echo data is imaged in two dimensions to obtain a fully polarized single-view complex image, such as Figure 4 As shown in the figure, it is a fully polarized SAR image of the imaging area, where (A) represents HH polarization, (B) represents HV polarization, (C) represents VH polarization, and (D) represents VV polarization. Then, using the first channel as the main channel, the single-view complex images are interferometrically registered to obtain the fully polarized single-view complex image after registration. In the main channel image, the microwave visual information belonging to different target structures in the two-dimensional SAR image is extracted, such as Figure 5 As shown. Then, the observation matrix is ​​established pixel by pixel, and the constraints of microwave vision information are constructed according to the microwave vision information. Based on the OMP algorithm, parallel sparse 3D imaging is performed in combination with the GPU to obtain the 3D point cloud of the target scene in this round. After iteration, the final converged 3D point cloud of the target scene is obtained, as shown in Figure 6 shown.

[0060] according to Figure 6 (A)-(B) It can be seen that in the iterative process, the point position of the 3D point cloud finally converges to a 3D point cloud result that is closer to the laser point cloud. Taking the laser point cloud as the true value, the 3D imaging accuracy of the traditional sparse 3D imaging algorithm and the method of the present invention is calculated, as shown in Table 2. It can be seen that with the assistance of microwave visual semantic information, even with 4 coherent observations, high-precision 3D imaging can be achieved, reaching 1.15 m under the data conditions of this embodiment.

[0061] Table 2

[0062]

[0063] In summary, the synthetic aperture radar microwave vision 3D imaging method proposed in the present invention has high-precision 3D imaging capability under a small number of observation conditions. Compared with the traditional method, it makes full use of the microwave vision information in the SAR image and point cloud, improves the imaging accuracy, and enhances the interpretability of the point cloud.

[0064] Accordingly, the present invention provides a synthetic aperture radar microwave vision three-dimensional imaging device, which includes various units capable of performing the aforementioned steps, specifically including:

[0065] A data acquisition unit is used to collect M groups of coherent fully polarized echo data for the target scene, and complete two-dimensional SAR imaging of the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images;

[0066] A constraint construction unit is used to perform interferometric image registration on M single-view complex images; extract microwave visual information from the registered M single-view complex images; establish an observation matrix pixel by pixel, and construct constraints on the microwave visual information based on the extracted microwave visual information;

[0067] The final 3D point cloud construction unit is used to perform parallel sparse 3D imaging based on the OMP algorithm in combination with a graphics processor GPU to obtain a 3D point cloud of the target scene rendered with polarization pseudo-color, and to determine whether the distance between the coordinates of each point of the 3D point cloud obtained in this round and the coordinates of each point of the 3D point cloud obtained in the previous round is less than a convergence threshold. If it is less than the convergence threshold, the iteration is terminated to obtain the final 3D point cloud of the target scene; otherwise, from the 3D point cloud of the polarization pseudo-color rendered target scene obtained, geometric semantic information belonging to different target structures is extracted by line and surface detection methods, polarization semantic information belonging to different target structures is extracted using polarization pseudo-color information, the affiliation of the extracted different target structures is mapped to a 2D single-view complex image, and the constraints of the microwave visual information are reconstructed; the 3D point cloud that has completed the iteration is screened and filtered to obtain the final 3D point cloud of the target scene.

[0068] In a third aspect, the present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned synthetic aperture radar microwave vision three-dimensional imaging method.

[0069] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned synthetic aperture radar microwave vision three-dimensional imaging method.

[0070] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0071] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0072] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. A synthetic aperture radar microwave vision three-dimensional imaging method, characterized in that: The method comprises the following steps: Step 1: collect M groups of coherent fully polarized echo data for the target scene, and perform two-dimensional SAR imaging on the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images; Step 2, performing interferometric image registration on the M single-view complex images; Step 3, extracting microwave visual information from the registered M single-view complex images; Step 4: Establish an observation matrix pixel by pixel, and construct constraints of microwave vision information based on the extracted microwave vision information; Step 5: Based on the OMP algorithm, parallel sparse 3D imaging is performed in combination with a graphics processor (GPU) to obtain a 3D point cloud of the target scene rendered in polarization pseudo-color. Step 6: Determine whether the distance between the coordinates of each point in the 3D point cloud obtained in this round and the coordinates of each point in the 3D point cloud obtained in the previous round is less than the convergence threshold. If it is less than the convergence threshold, end the iteration and go to step 9; otherwise, go to step 7. Step 7: extracting geometric semantic information belonging to different target structures from the three-dimensional point cloud rendered by polarization pseudo-color of the target scene obtained in step 5 by line and surface detection methods; and extracting polarization semantic information belonging to different target structures by using polarization pseudo-color information; Step 8, mapping the affiliation relationship of different target structures extracted in step 7 to the two-dimensional single-view complex image, and returning to step 4; Step 9: Screen and filter the iterated 3D point cloud to obtain the final 3D point cloud of the target scene. Wherein, in step 4, the microwave visual information constraint constructed is: ， ， in, Indicates the slant height of the scattering point, agrmin indicates the minimum value, represents the inversion residual of the scattering vector, represents the observation vector, represents the observation matrix, represents the pseudo-inverse of the observation matrix, Indicates the mean , the covariance matrix is The multivariate normal probability density function of represents the inversion error at the signal level, Indicates the mean , the covariance matrix is The normal probability density function of represents the geometric semantic constraints. Indicates the mean , the covariance matrix is The normal probability density function of represents the polarization semantic constraint, represents the backscattering coefficient; The step 5 comprises the following steps: Step 5.1, data preparation and segmentation: divide the input observation data into multiple pixels, and perform 3D reconstruction for each pixel independently, and upload the divided observation data to the GPU memory; Step 5.2: Initialization of the OMP algorithm: For each pixel, initialize the observation matrix , observation vector and the residual vector , the GPU operates on all pixels in parallel and allocates computing resources; Step 5.3, iterative sparse signal solution: For each pixel, the OMP algorithm iteratively searches for the observation matrix Columns such that the current column is consistent with the residual vector The inner product of is the largest, including: calculating each candidate basis vector and the residual vector The correlation, that is, the projection calculation , select from all projections the same as the residual vector The most relevant basis vector, add it to the active set; Step 5.4, residual update: For each iteration of the selected basis vector, the GPU updates the sparse solution by solving the linear equation , and recalculate the updated residual vector ; Step 5.5, Iteration stop condition: After each pixel completes several iterations, the GPU checks the residual vector Whether it is lower than the set threshold, or whether the number of non-zero items of the sparse vector reaches the preset upper limit. If any stopping condition is met, the OMP algorithm terminates and the sparse solution of the corresponding pixel is obtained; Step 5.6, 3D point cloud reconstruction: According to the sparse solution obtained by the OMP algorithm, the 3D coordinates of each pixel are reconstructed into a sparse 3D point cloud; Step 5.7, polarization pseudo-color rendering: Process and render the illumination, texture, and polarization information of the sparse 3D point cloud data through the GPU to generate a polarization pseudo-color 3D point cloud; Step 5.8, data transmission and result saving: The polarization pseudo-color 3D point cloud data calculated in the GPU is transmitted back to the host memory, and the result is saved as a 3D point cloud file.

2. The synthetic aperture radar microwave vision three-dimensional imaging method according to claim 1, characterized in that: The step 2 includes determining a main channel and registering other channel images with the main channel image using an interference image registration method.

3. The synthetic aperture radar microwave vision three-dimensional imaging method according to claim 2, characterized in that: The interference image registration method includes a maximum spectrum method.

4. The synthetic aperture radar microwave vision three-dimensional imaging method according to claim 1, characterized in that: The microwave visual information includes geometric semantic information and polarization semantic information.

5. The synthetic aperture radar microwave vision three-dimensional imaging method according to claim 4, characterized in that: The step 3 includes extracting geometric semantic information belonging to different target structures in the two-dimensional SAR image using line and surface detection methods in the main channel image; generating a Pauli pseudo-color image using a fully polarized single-view complex image, and extracting polarization semantic information belonging to different target structures from the image based on polarization scattering features, wherein the polarization scattering features include neighborhood consistency, continuity, and intra-class correlation.

6. A synthetic aperture radar microwave vision three-dimensional imaging device, used to implement a synthetic aperture radar microwave vision three-dimensional imaging method according to any one of claims 1 to 5, characterized in that: include: A data acquisition unit is used to collect M groups of coherent fully polarized echo data for the target scene, and complete two-dimensional SAR imaging of the fully polarized echo data to obtain M pairs of coherent fully polarized single-view complex images; A constraint construction unit is used to perform interferometric image registration on the M single-view complex images; and extract microwave visual information from the registered M single-view complex images; Establish an observation matrix pixel by pixel, and construct constraints of microwave vision information based on the extracted microwave vision information; The final 3D point cloud construction unit is used to perform parallel sparse 3D imaging based on the OMP algorithm in combination with the graphics processor GPU to obtain the polarization pseudo-color rendered 3D point cloud of the target scene, and to determine whether the distance between the coordinates of each point of the 3D point cloud obtained in this round and the coordinates of each point of the 3D point cloud obtained in the previous round is less than the convergence threshold. If it is less than the convergence threshold, the iteration is terminated to obtain the final 3D point cloud of the target scene; Otherwise, the geometric semantic information belonging to different target structures is extracted from the three-dimensional point cloud rendered by polarization pseudo-color of the acquired target scene by line and surface detection methods, and the polarization semantic information belonging to different target structures is extracted by using the polarization pseudo-color information. The affiliation relationship of the extracted different target structures is mapped to the two-dimensional single-view complex image, and the constraints of the microwave visual information are reconstructed; The iterated 3D point cloud is screened and filtered to obtain the final 3D point cloud of the target scene.

7. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement a synthetic aperture radar microwave vision three-dimensional imaging method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by a processor, the processor can implement a synthetic aperture radar microwave vision three-dimensional imaging method as described in any one of claims 1-5.

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