Three-dimensional human body measurement method and device based on 3D structured light and double-layer surface model

Abstract

The present application discloses a three-dimensional human body measurement method and device based on 3D structured light and a double-layer surface model. The inner model in the double-layer surface model captures the basic form and posture of the human body, and the outer model captures the detailed features of non-rigid objects on the surface of the human body. A 3D structured light camera module is used to emit light with structural features to the surface of the human body to be measured, and the light reflected from the human body surface is captured to generate depth map data of the human body to be measured, which is further converted into a three-dimensional point cloud data model. In the process of matching the three-dimensional point cloud data model with the double-layer surface model, the double-layer surface model is adjusted based on the data difference and the double-layer node graph of the double-layer surface model. Finally, the three-dimensional measurement data of the human body to be measured is obtained based on the adjusted double-layer surface model. The present application uses 3D structured light and a double-layer surface model to accurately reconstruct the three-dimensional form of the human body and attached objects, which not only achieves three-dimensional precise measurement, but also significantly reduces the measurement cost compared to 3D scanning equipment.

Description

Technical Field

[0001] The present application relates to the field of computer vision technology, and in particular to a three-dimensional human body measurement method and device based on 3D structured light and a double-layer surface model. Background Art

[0002] Traditional anthropometric methods, including manual measurement or 2D image-based techniques, suffer from inaccuracies and cumbersome operation. Manual measurement methods rely on the operator's skill and experience, which can easily lead to human error. 2D measurement techniques cannot accurately capture the body's three-dimensional form, limiting the accuracy and reliability of measurement results. These limitations are particularly prominent in industries such as medicine, sports, and fashion design, where extremely high measurement accuracy is required to ensure customized products and services. For example, in the medical field, measured body dimensions are used to customize orthopedics and prostheses for patients. In sports science, measured athlete dimensions are used to optimize training plans and competitive performance, such as providing body shaping advice or customizing athletic apparel. In fashion design, body shape data is used to support personalized clothing design and customization, improving comfort and fit. While 3D scanning devices can provide highly accurate 3D anthropometric data, such equipment is typically expensive. Summary of the Invention

[0003] Based on the above problems, this application provides a three-dimensional human body measurement method and device based on 3D structured light and double-layer surface model, aiming to accurately reconstruct the three-dimensional shape of the human body and attached objects by using 3D structured light and double-layer surface model, and achieve three-dimensional accurate measurement of the human body at low cost.

[0004] The embodiments of this application disclose the following technical solutions:

[0005] In a first aspect, the present application provides a three-dimensional human body measurement method based on 3D structured light and a double-layer surface model, wherein the double-layer surface model includes an inner layer model and an outer layer model, wherein the inner layer model is used to capture the basic form and posture of the human body, and the outer layer model is used to capture the detailed features of non-rigid objects on the human body surface; the method comprises:

[0006] The 3D structured light camera module emits light with structural features to the surface of the human body to be measured, captures the light reflected by the surface of the human body to be measured, and generates depth map data of the human body to be measured based on the light reflected by the surface of the human body to be measured;

[0007] Establishing a three-dimensional point cloud data model of the human body to be measured according to the depth map data;

[0008] Matching the three-dimensional point cloud data model with the double-layer surface model, adjusting the double-layer surface model based on differences between the double-layer surface model and the three-dimensional point cloud data model and a double-layer node graph of the double-layer surface model during the matching process, and obtaining an adjusted double-layer surface model after the matching process is completed; the double-layer node graph includes: body nodes and far-body nodes, wherein the body nodes correspond to the inner layer model and the far-body nodes correspond to the outer layer model;

[0009] Three-dimensional measurement data of the human body to be measured is obtained according to the adjusted double-layer surface model.

[0010] In an optional implementation of the first aspect, adjusting the double-layer surface model based on a difference between the double-layer surface model and the three-dimensional point cloud data model and a double-layer node graph of the double-layer surface model during the matching process includes:

[0011] Matching the inner layer model with the three-dimensional point cloud data model to obtain an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjusting the parameters of the inner layer model based on the error;

[0012] Based on the matched inner layer model, the point cloud data in the three-dimensional point cloud data model is integrated to match the outer layer model, and the parameters of the outer layer model are adjusted during the matching process;

[0013] Synchronously binding and data fusing the matched inner layer model and the matched outer layer model, dynamically adjusting the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model, and optimizing the parameters of the double-layer surface model based on the effect of the joint motion tracking;

[0014] It is determined whether the current double-layer surface model meets the model optimization cutoff condition. If so, the adjustment of the model parameters is stopped to obtain the adjusted double-layer surface model.

[0015] In an optional implementation of the first aspect, matching the inner layer model with the three-dimensional point cloud data model, obtaining an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjusting parameters of the inner layer model based on the error includes:

[0016] The parameters of the inner layer model are adjusted with the minimization of the energy function as the optimization goal; the energy function includes: a data term, a regularization term and a priori term; the data term is used to measure the difference between the inner layer model and the three-dimensional point cloud data model; the regularization term is used to constrain the degree of deformation of the inner layer model; the priori term is used to guide the convergence of the inner layer model by using prior knowledge of human body shape and posture.

[0017] In an optional implementation of the first aspect, dynamically adjusting the two-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the two-layer surface model includes:

[0018] Controlling the rotation of the rotating device to drive the human body under test located on the rotating device to rotate synchronously; the depth map data specifically includes: a continuous depth map data stream generated based on light reflected from the surface of the human body under test during the period of synchronous rotation with the rotating device;

[0019] The double-layer surface model is controlled to rotate synchronously with the rotating device so that the double-layer surface model is matched with the corresponding target point cloud data in the three-dimensional point cloud data model at a target angle, and the parameters of the inner layer model and the parameters of the outer layer model are optimized; the target point cloud data is point cloud data converted based on the target depth map in the depth map continuous data stream; the target depth map is a depth map generated based on the light reflected from the surface when the human body to be measured is rotated to the target angle.

[0020] In an optional implementation of the first aspect, determining whether the current double-layer surface model satisfies a model optimization cutoff condition includes:

[0021] Determining whether the matching quality of the current double-layer surface model is qualified, and determining whether the joint motion tracking effect of the current double-layer surface model is qualified;

[0022] If the matching quality is qualified and the effect of the joint motion tracking is qualified, it is determined that the current double-layer surface model meets the model optimization cutoff condition;

[0023] If the matching quality is unqualified, or the effect of the joint motion tracking is unqualified, it is determined that the current double-layer surface model does not meet the model optimization cutoff condition.

[0024] In an optional implementation of the first aspect, determining whether the matching quality of the current two-layer surface model is qualified includes:

[0025] If the matching error of the current double-layer surface model is less than or equal to the preset error threshold, and the surface of the current double-layer surface model is visually inspected to be smooth and consistent with the point cloud, then the matching quality of the current double-layer surface model is determined to be qualified;

[0026] If the matching error of the current double-layer surface model is greater than the preset error threshold, and a visual inspection shows that a key area of ​​the current double-layer surface model is deformed or broken, the matching quality of the current double-layer surface model is determined to be unqualified.

[0027] In an optional implementation of the first aspect, determining whether an effect of the joint motion tracking of the current two-layer surface model is satisfactory includes:

[0028] If the current dual-layer surface model is stable and always consistent with the point cloud when moving at a preset speed or performing a gesture of a preset complexity, the model is matched consistently with the point cloud across multiple frames, and the motion tracking accuracy is greater than a preset accuracy threshold, then the joint motion tracking effect of the current dual-layer surface model is considered qualified;

[0029] If the current double-layer surface model slides or misaligns when moving at a preset speed or performing a posture of preset complexity, causing the model to not match the actual posture, and if there is a freeze or distortion during the motion tracking process, then the joint motion tracking effect of the current double-layer surface model is judged to be unqualified.

[0030] In an optional implementation of the first aspect, establishing a three-dimensional point cloud data model of the human body to be measured based on the depth map data includes:

[0031] Performing denoising and enhancement processing on the depth map data;

[0032] The denoised and enhanced depth map data is converted into a 3D point cloud data model of the human body to be measured using a 3D reconstruction algorithm.

[0033] In an optional implementation of the first aspect, while the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes:

[0034] Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit;

[0035] The method of converting the denoised and enhanced depth map data into a three-dimensional point cloud data model of the human body to be measured by using a three-dimensional reconstruction algorithm includes:

[0036] Using a 3D reconstruction algorithm, the denoised and enhanced depth map data is converted into preliminary point cloud data;

[0037] The position information and / or the posture information are used to provide constraints for point cloud registration, and preliminary point cloud data from multiple perspectives are registered and fused to form a three-dimensional point cloud data model of the human body to be measured.

[0038] In an optional implementation of the first aspect, while the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes:

[0039] Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit;

[0040] When matching the three-dimensional point cloud data model with the double-layer surface model, the dynamic parameters of the double-layer surface model are optimized using the position information and / or the posture information.

[0041] In a second aspect, the present application provides a three-dimensional human body measurement device based on 3D structured light and a double-layer surface model, wherein the double-layer surface model includes an inner layer model and an outer layer model, wherein the inner layer model is used to capture the basic form and posture of the human body, and the outer layer model is used to capture the detailed features of non-rigid objects on the surface of the human body; the device includes:

[0042] a depth map generation module, configured to emit light having structural features to the surface of a human body to be measured through a 3D structured light camera module, capture the light reflected from the surface of the human body to be measured, and generate depth map data of the human body to be measured based on the light reflected from the surface of the human body to be measured;

[0043] A point cloud conversion module is used to establish a three-dimensional point cloud data model of the human body to be measured based on the depth map data;

[0044] a model matching and adjustment module, configured to match the three-dimensional point cloud data model with the double-layer surface model; during the matching process, adjusting the double-layer surface model based on differences between the double-layer surface model and the three-dimensional point cloud data model and a double-layer node graph of the double-layer surface model; and obtaining an adjusted double-layer surface model after the matching process is completed; the double-layer node graph comprising: in-body nodes and far-body nodes, wherein the in-body nodes correspond to the inner-layer model and the far-body nodes correspond to the outer-layer model;

[0045] The measurement data acquisition module is used to obtain three-dimensional measurement data of the human body to be measured based on the adjusted double-layer surface model.

[0046] Compared with the existing technology, this application has the following beneficial effects:

[0047] This application discloses a three-dimensional human body measurement method and device based on 3D structured light and a double-layer surface model. A 3D structured light camera module emits light with structural features onto the surface of the human body to be measured, and captures the light reflected from the human body surface to generate depth map data of the human body to be measured, which is further converted into a three-dimensional point cloud data model. During the matching process between the three-dimensional point cloud data model and the double-layer surface model, the double-layer surface model is adjusted based on data differences and the double-layer node graph of the double-layer surface model. Since the inner layer model of the double-layer surface model can capture the basic shape and posture of the human body, and the outer layer model can capture the detailed features of non-rigid objects on the human body surface, the double-layer surface model is fitted and matched with the three-dimensional point cloud data model. The model parameters can be adjusted from both the inner and outer aspects to approximate the human body features reflected by the three-dimensional point cloud data model. Finally, three-dimensional measurement data of the human body to be measured is obtained based on the adjusted double-layer surface model. By using 3D structured light and a double-layer surface model to accurately reconstruct the three-dimensional shape of the human body and attached objects, this application not only achieves three-dimensional precision measurement, but also significantly reduces measurement costs compared to 3D scanning equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A flowchart of a three-dimensional human body measurement method based on 3D structured light and a double-layer surface model provided in an embodiment of the present application;

[0050] Figure 2 A flowchart of an implementation method for matching and adjusting a double-layer surface model based on a three-dimensional point cloud data model provided in an embodiment of the present application;

[0051] Figure 3 A schematic structural diagram of a three-dimensional body measurement device based on 3D structured light and a double-layer surface model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] As described above, traditional human body measurement methods have poor accuracy and reliability. Although 3D scanning equipment has high accuracy, the measurement cost is high and it is not easy to promote. In view of the above problems, the inventors have conducted research and provided a three-dimensional human body measurement method and device based on 3D structured light and a double-layer surface model in this application. With the help of a 3D structured light camera module, the light reflected from the human body surface is captured, and depth map data with depth information is formed. Based on this, it is converted into a three-dimensional point cloud data model for fitting and matching with a double-layer surface model. In the technical solution of this application, a double-layer surface model is used to fit the parameters of human body features, wherein the inner model captures the basic form and posture of the human body, and the outer model captures the detailed features of non-rigid objects on the surface of the human body. As a result, the fitted double-layer surface model can reflect both the essential characteristics of the human body to be measured and the detailed characteristics, thereby ensuring the accuracy and reliability of the three-dimensional measurement data finally obtained.

[0053] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0054] See also Figure 1 , which is a flow chart of a three-dimensional human body measurement method based on 3D structured light and a double-layer surface model provided in an embodiment of the present application. Figure 1 As shown, the 3D human body measurement method based on 3D structured light and double-layer surface model includes:

[0055] S101, emitting light with structural features to the surface of a human body to be measured through a 3D structured light camera module, capturing the light reflected from the surface of the human body to be measured, and generating depth map data of the human body to be measured based on the light reflected from the surface of the human body to be measured.

[0056] The light emitted by the 3D structured light camera module has structural features. For example, it can be a grid-like or striped pattern of infrared light. Structural features can be achieved through pattern settings. For example, different lighting patterns can be pre-configured. When the user selects a specific lighting pattern, infrared light with the specific structural features of that pattern will be emitted toward the surface of the human body being measured.

[0057] In an embodiment of the present application, a 3D structured light camera module includes a transmitter and a receiver. The receiver is equipped with a lens assembly and an image sensor. The transmitter emits infrared light toward the human body to be measured, and the image sensor is used to collect the reflected light transmitted through the lens assembly and perform photoelectric conversion on it. The converted electrical signal is used to generate an infrared image. The 3D structured light camera module can also be configured with a processing chip that can convert the infrared image into depth map data. In addition, multiple 3D structured light camera modules can also be configured in the scene. Combining multiple modules can cover a wider field of view, thereby meeting the needs of three-dimensional human body measurement of more different body shapes and postures.

[0058] S102: Establish a three-dimensional point cloud data model of the human body to be measured according to the depth map data.

[0059] In practical applications, computer vision technology can be used to generate a 3D point cloud data model based on the collected depth map data. Since converting depth map data into point cloud data based on computer vision technology is a relatively mature technology, it will not be discussed in detail here.

[0060] S103. Match the three-dimensional point cloud data model with the double-layer surface model. During the matching process, adjust the double-layer surface model based on the difference between the double-layer surface model and the three-dimensional point cloud data model and the double-layer node graph of the double-layer surface model, and obtain the adjusted double-layer surface model after the matching process is completed.

[0061] Because the 3D point cloud data model is based on the collected reflected light and converted using depth information, it can accurately reflect the characteristics of the human body under test from a spatial perspective. After completing the construction of the 3D point cloud data model of the human body under test in step S102, step S103 uses the 3D point cloud data model as a template and references it to fit the parameters of an initialized two-layer surface model. This ensures that the two-layer surface model, through parameter optimization during the fitting and matching process, can also accurately reflect the characteristics of the human body under test.

[0062] The dual-layer surface model consists of an inner model and an outer model. The inner model captures the basic form and posture of the human body, while the outer model captures the detailed features of non-rigid objects on the human surface. The dual-layer node graph of the dual-layer surface model expresses the structural connections between nodes on the inner and outer models. Specifically, the dual-layer node graph includes in-body nodes and far-body nodes, where the in-body nodes correspond to the inner model and the far-body nodes correspond to the outer model.

[0063] In-body nodes are responsible for representing the human skeleton morphology. Remote-body nodes are responsible for describing external details of the human body, such as the shape of clothing. In addition to its basic components (in-body nodes and remote-body nodes), the two-layer node graph also includes additional information. This additional information includes the connection relationships between nodes, such as the connection relationship between in-body nodes and in-body nodes, the connection relationship between remote-body nodes and remote-body nodes, and the connection relationship between in-body nodes and remote-body nodes. Furthermore, the additional information can also include constraint rules, which are used to ensure the consistency between the inner model and the outer model.

[0064] Regarding the specific implementation of matching the three-dimensional point cloud data model with the double-layer surface model mentioned in this step S103, it can be to match the inner layer model first, and then match the outer layer model after the inner layer model is matched. This is because the inner layer model is used to capture the basic form of the human body. Matching the inner layer model first can provide a stable and relatively accurate foundation for the outer layer model. The outer layer model focuses on the details of the human body surface. Based on the matching results of the inner layer model, the outer layer model is further matched, and the captured details of the human body surface are not easily distorted. Figure 2 , which introduces this example implementation.

[0065] Figure 2 This is a flow chart of an implementation method for matching and adjusting a double-layer surface model based on a three-dimensional point cloud data model provided in an embodiment of the present application. Figure 2 As shown, adjusting the double-layer surface model based on the difference between the double-layer surface model and the three-dimensional point cloud data model and the double-layer node graph of the double-layer surface model during the matching process may include:

[0066] S1031. Match the inner layer model with the three-dimensional point cloud data model to obtain an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjust the parameters of the inner layer model based on the error.

[0067] In one possible implementation of step S1031, the error between the inner layer model and the 3D point cloud data model is quantified using a data term derived from an energy function. Furthermore, the regularization term of the energy function is used to constrain model deformation, and the prior term of the energy function is used to guide model convergence. Specifically, the parameters of the inner layer model can be adjusted to minimize the value of the energy function.

[0068] As mentioned above, the energy function includes a data term, a regularization term, and a priori term. The data term measures the difference between the inner model and the 3D point cloud data model. The regularization term constrains the deformation of the inner model. The priori term leverages prior knowledge of human form and posture to guide the convergence of the inner model.

[0069] Minimizing the energy function, which includes the aforementioned data terms, regularization terms, and prior terms, as the optimization objective for the inner model helps achieve rapid convergence in model fitting, narrowing the gap between the parameter-adjusted inner model and the 3D point cloud data model, achieving a more ideal matching effect. The inner model truly reflects the basic form of the human body being measured. Next, the outer model can be matched in S1032.

[0070] S1032: Based on the matched inner layer model, the point cloud data in the three-dimensional point cloud data model is integrated to perform matching with the outer layer model, and the parameters of the outer layer model are adjusted during the matching process.

[0071] In this application, the outer layer model is matched based on the inner layer model that has already been matched. The outer layer model is matched by integrating the point cloud data representing the surface details of the human body in the 3D point cloud data model, so that the outer layer model can accurately capture the surface details of the human body when optimizing and adjusting the parameters.

[0072] S1033. Synchronously bind and fuse the matched inner layer model and the matched outer layer model, and dynamically adjust the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model, and optimize the parameters of the double-layer surface model based on the effect of the joint motion tracking.

[0073] In this embodiment of the present application, the matched inner and outer models need to be synchronously bound. Synchronous binding is a prerequisite for joint motion tracking optimization. Through synchronous binding, the inner and outer models are aligned and bound to each other. This ensures parameter optimization during the joint motion tracking phase.

[0074] In the embodiments of the present application, joint motion tracking of a dual-layer surface model is primarily performed to evaluate whether the parameter-adjusted and optimized dual-layer surface model accurately reflects the motion characteristics of the human body under test. For example, the dual-layer surface model can maintain good performance under rapid human motion or complex posture changes. Below, an example implementation of joint motion tracking of a dual-layer surface model is described, in which the dual-layer node graph is dynamically adjusted based on the motion characteristics of the three-dimensional point cloud data model.

[0075] In an embodiment of the present application, the human body to be measured is placed on a rotating device during the measurement phase. The rotating device can rotate under the drive of a motor, and thereby cause the human body to be measured to rotate as well. For example, when the rotating device rotates 180°, the human body to be measured also rotates 180°. During the rotation of the rotating device, for measurement requirements, the human body to be measured can be required to remain relatively still relative to the rotating device according to instructions, or the human body to be measured can be required to make certain specific postures (such as opening arms, waving arms, kicking legs, etc.) while rotating with the rotating device.

[0076] In an embodiment of the present application, in order to achieve joint motion tracking of the double-layer surface model, the rotating device is controlled to rotate and drive the human body to be measured located on the rotating device to rotate synchronously; the double-layer surface model is controlled to rotate synchronously with the rotating device, so that the double-layer surface model is matched with the corresponding target point cloud data in the three-dimensional point cloud data model at a target angle, and the parameters of the inner layer model and the parameters of the outer layer model are optimized.

[0077] While driving the subject to be measured on the rotating device to rotate synchronously, the 3D structured light camera module continuously emits infrared light toward the subject through the transmitter and collects the infrared light reflected from the rotating subject's surface through the receiver, thereby generating a continuous infrared image. A continuous depth map data stream can then be generated based on the continuous infrared image. For example, the continuous depth map data stream includes depth map data generated for each frame of infrared image collected during the subject's rotation. Because the depth map data is presented as a continuous data stream, the constructed 3D point cloud data model also reflects the dynamic characteristics of the subject's rotation. When performing joint motion tracking, it is necessary to synchronize the dual-layer surface model with the subject's rotation. This allows the established 3D point cloud data model to be matched with the dual-layer surface model on an angle-by-angle (frame-by-frame) basis, achieving joint motion tracking. For example, the target depth map is a depth map generated based on light reflected from the subject's surface when the subject is rotated to the target angle. The target point cloud data is point cloud data converted from the target depth map in the continuous depth map data stream. In other words, both the target depth map and the target point cloud data correspond to the target angle. The double-layer surface model is rotated synchronously, so that the double-layer surface model can be matched with the target point cloud data when it is at a target rotation angle.

[0078] The goal of performing joint motion tracking on the dual-layer surface model is to optimize the model's parameters based on the results of the joint motion tracking. This ensures that the optimized dual-layer surface model exhibits highly accurate dynamic performance. The termination of model parameter optimization is determined using the model optimization cutoff condition. See step S1034 for details.

[0079] S1034: Determine whether the current double-layer surface model meets the model optimization cutoff condition. If so, proceed to step S1035.

[0080] Exemplarily, the model optimization cutoff condition for the dual-layer surface model may include two specific conditions: one condition regarding the model's matching quality, and the other condition regarding the model's dynamic performance. Specifically, a determination is made as to whether the matching quality of the current dual-layer surface model is satisfactory, and also as to whether the joint motion tracking effect of the current dual-layer surface model is satisfactory. If the matching quality is satisfactory and the joint motion tracking effect is satisfactory, the current dual-layer surface model is determined to meet the model optimization cutoff condition. If the matching quality is unsatisfactory or the joint motion tracking effect is unsatisfactory, the current dual-layer surface model is determined to not meet the model optimization cutoff condition.

[0081] In other possible implementations, the model optimization cutoff condition can also be a unilateral condition. For example, if the model's matching quality is satisfactory, the model optimization cutoff condition is satisfied. Alternatively, if the model's joint motion tracking effect is satisfactory, the model optimization cutoff condition is satisfied. Furthermore, the model optimization cutoff condition can also involve the number of times the model parameter optimization is performed. By limiting the number of times, the model parameter optimization is stopped.

[0082] Below, by way of example, an implementation method for determining whether the matching quality of the current double-layer surface model is qualified is introduced.

[0083] If the matching error of the current dual-layer surface model is less than or equal to a preset error threshold, and a visual inspection shows that the surface of the current dual-layer surface model is smooth and consistent with the point cloud, then the matching quality of the current dual-layer surface model is determined to be acceptable. For example, if the average error from the point cloud to the surface of the dual-layer surface model is less than a preset error threshold, then the matching quality is determined to be acceptable.

[0084] If the matching error of the current dual-layer surface model is greater than the preset error threshold, and a visual inspection shows deformation or fracture in key areas of the current dual-layer surface model, the matching quality of the current dual-layer surface model is determined to be unqualified. For example, if the average error between the point cloud and the surface of the dual-layer surface model is greater than the preset error threshold, the surface has a large deviation and the matching quality of the model is unqualified.

[0085] Below, by way of example, an implementation method for determining whether the joint motion tracking effect of the current double-layer surface model is qualified is introduced.

[0086] If the current double-layer surface model moves at a preset speed or performs a posture of preset complexity, the model update is stable and always consistent with the point cloud, the model matches the point cloud coherently across multiple frames, and the motion tracking accuracy is greater than the preset accuracy threshold, then the joint motion tracking effect of the current double-layer surface model is judged to be qualified. Among them, the preset speed is used to distinguish the speed of movement and can be customized; the preset complexity is used to distinguish the high and low complexity of the posture and can also be customized. As an example, in fast motion or complex postures (such as dance performances or sports activities), the model update is stable and always consistent with the point cloud, the model and the point cloud match coherently across multiple frames, and the dynamic tracking accuracy is high, then the joint motion tracking effect of the model is determined to be qualified.

[0087] If the current dual-layer surface model experiences slippage or misalignment when moving at a preset speed or performing a gesture of a preset complexity, causing the model to mismatch the actual gesture, or causing stuttering or distortion during motion tracking, the joint motion tracking of the current dual-layer surface model is deemed unsatisfactory. For example, if the model experiences slippage or misalignment, causing the model to mismatch the actual gesture, or causing stuttering or distortion during motion tracking, this indicates that the model cannot meet dynamic real-time and accuracy requirements. Therefore, model parameters need to be adjusted and optimized, and the current joint tracking of the dual-layer surface model is unstable or does not achieve the expected accuracy.

[0088] S1035: Stop adjusting the model parameters to obtain an adjusted double-layer surface model.

[0089] Through the above steps S1031 to S1035, the optimization adjustment of the model parameters is completed, and the adjusted double-layer surface model that meets the expectations is obtained. Next, the process proceeds to S104.

[0090] S104: Obtain three-dimensional measurement data of the human body to be measured according to the adjusted double-layer surface model.

[0091] As a parametric human body model, the double-layer surface model uses parameters to represent the body's shape and posture. Therefore, once the adjusted double-layer surface model is obtained, the accurate body information contained in the model can be used to calculate specific measurements representing the body's three-dimensional features. For example, specific dimensions such as height, weight, and body proportions can be calculated. This can be used for subsequent applications in areas such as clothing design and motion analysis.

[0092] The three-dimensional human body measurement method based on 3D structured light and a double-layer surface model described above uses a 3D structured light camera module to emit light with structural features to the surface of the human body to be measured, and captures the light reflected from the human body surface to generate depth map data of the human body to be measured, which is further converted into a three-dimensional point cloud data model. During the matching process between the three-dimensional point cloud data model and the double-layer surface model, the double-layer surface model is adjusted based on the data differences and the double-layer node graph of the double-layer surface model. Since the inner layer model of the double-layer surface model can capture the basic shape and posture of the human body, and the outer layer model can capture the detailed features of non-rigid objects on the human body surface, the double-layer surface model is fitted and matched with the three-dimensional point cloud data model. The model parameters can be adjusted from both the inner and outer aspects to approximate the human body features reflected by the three-dimensional point cloud data model. Finally, three-dimensional measurement data of the human body to be measured is obtained based on the adjusted double-layer surface model. This application uses 3D structured light and a double-layer surface model to accurately reconstruct the three-dimensional shape of the human body and attached objects, not only achieving three-dimensional precision measurement, but also significantly reducing measurement costs compared to 3D scanning equipment.

[0093] Furthermore, the embodiment of the present application also proposes a specific implementation process for establishing a three-dimensional point cloud data model. First, the depth map data is denoised and enhanced. In this way, the data quality of the depth map data is improved. Next, a three-dimensional reconstruction algorithm, such as an iterative closest point algorithm, can be used to convert the processed data into a three-dimensional point cloud data model of the human body to be measured. Through optimization measures such as denoising and enhancement processing of the depth map data in the early stage, it is beneficial to achieve accurate conversion of the three-dimensional point cloud data model. This process ensures the accuracy and integrity of the data during the conversion process from two-dimensional images to three-dimensional models.

[0094] The present application also proposes that multimodal information can be comprehensively utilized to achieve accurate measurement of three-dimensional human body data. Two possible implementation methods of the comprehensive utilization of multimodal information are described below.

[0095] In a first possible implementation, while the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes:

[0096] Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit;

[0097] Furthermore, the 3D reconstruction algorithm is used to convert the denoised and enhanced depth map data into a 3D point cloud data model of the human body to be measured, including:

[0098] The denoised and enhanced depth map data is converted into preliminary point cloud data using a three-dimensional reconstruction algorithm. The position information and / or the posture information are used to provide constraints for point cloud registration, and the preliminary point cloud data from multiple perspectives are registered and fused to form a three-dimensional point cloud data model of the human body to be measured.

[0099] In the first possible implementation described above, the position and / or attitude information measured by the inertial measurement unit (IMU) is used as an example of fused modal information. This information provides auxiliary constraints for point cloud registration, facilitating more accurate model matching. This first possible implementation demonstrates the fusion of multimodal information during point cloud processing.

[0100] In a second possible implementation, while the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes:

[0101] Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit;

[0102] When matching the three-dimensional point cloud data model with the double-layer surface model, the dynamic parameters of the double-layer surface model are optimized using the position information and / or the posture information.

[0103] In the second possible implementation, the position and / or attitude information measured by the inertial measurement unit (IMU) is used as an example of fused modal information. This modal information provides guidance for dynamic parameter adjustment during model matching. This second possible implementation demonstrates the fusion of multimodal information during the model matching phase.

[0104] The additional position and / or attitude information provided by sensors, such as inertial measurement units, enhances the robustness of the system through multimodal fusion and allows high-precision human body measurement even in complex lighting conditions or occlusions.

[0105] It should be noted that the above examples illustrate the use of multimodal information fusion using position or posture measurements by an inertial measurement unit (IMU). The IMU can be installed at locations such as the collar or corner of a person's clothing, or at other locations on the person's body, without limitation. Furthermore, sensors used to collect multimodal information are not limited to IMUs; they can also be sensors of other types or functions.

[0106] In the field of human body modeling, a parameterized common model refers to a parameter-based human body model that can describe and simulate the human body shape and posture of different individuals through a set of parameters. This model is usually constructed based on a large amount of human body scan data, which can capture the main variation characteristics of the human body and express these characteristics in a parameterized form. The main advantages of the parameterized common model are its flexibility and universality, and it can adapt to people of different genders, ages and body shapes. In this embodiment of the present application, a double-layer surface model adopts a parameterized model with a double-layer structure (inner layer and outer layer). In this embodiment of the present application, the double-layer surface model is responsible for accurately capturing the basic form of the human body. The model is adjusted to match the human body data of a specific individual through a series of predefined parameters, such as body shape parameters, posture parameters, etc. These parameters can describe the main geometric and motion characteristics of the human body, so that the model can not only adapt to the static human body shape, but also simulate dynamic posture changes.

[0107] The inner model, such as the SMPL model, is the core of the double-layer surface model. It accurately captures the basic form and dynamic posture changes of the human body through predefined parameters (such as body shape parameters and posture parameters). The outer model is responsible for capturing the dynamic non-rigid objects of the human body, such as clothing, and realizes the overall reconstruction of the human body and its attached objects through gradually integrated surface data. This design enables the model to not only adapt to the static human body shape, but also simulate dynamic posture changes and external expressions. The design of the double-layer surface model reflects the advantages of the parametric public model. Through the collaborative work of the inner and outer layers, it realizes the comprehensive capture and accurate reconstruction of the human body and its attached objects.

[0108] This technology makes human body measurement and 3D reconstruction faster and more accurate, automating operations and significantly improving data reliability and measurement efficiency. It has a wide range of applications, including healthcare, sports science, fashion design, and virtual reality. Its value is particularly evident in scenarios requiring high-precision, real-time, dynamic reconstruction.

[0109] In general, this application combines modern optical scanning technology and computer modeling technology to propose an innovative and efficient three-dimensional human body reconstruction solution. Its innovation lies in the ability to capture the three-dimensional form of the human body in real time and use a double-layer surface model to jointly model the human body and attached objects. This process not only improves the accuracy of the measurement, but also greatly reduces the required measurement time. In addition, this technology is extremely user-friendly, making it easy for even non-professionals to operate, further broadening its scope of application. Specifically, an intuitive and easy-to-use software interface has been developed, allowing operators to easily set scanning parameters, start and stop the scanning process, and view measurement results in real time. In addition, the association with business needs simplifies the operating process and lowers the technical threshold, allowing non-professionals to use it effectively, thereby expanding the scope of application and market acceptance of the technology.

[0110] The technical solution adopted in this application is not limited to static human body measurement, but can also be applied to the capture of dynamic scenes, such as human body interaction in virtual reality (VR) and augmented reality (AR), as well as dynamic character creation in the film and gaming industries. Through these innovative applications, not only the accuracy and efficiency of human body measurement are improved, but also its application potential in multiple fields is expanded. The development of this technology indicates that there will be more breakthroughs and innovations in the future in the capture of human body motion and the creation of virtual characters.

[0111] The entire system adopts a modular design, which is easy to maintain and upgrade. Each component such as the transmitter, receiver and processing software is designed as an independent module, which can be easily configured and adjusted according to specific needs. The modular design not only improves the flexibility and adaptability of the system, but also reduces costs, making this advanced technology easier to promote and apply. Through this comprehensive technical solution, the new system not only solves the accuracy and efficiency problems in existing measurement technologies, but also meets the needs of modern high-speed development industries with its user-friendly design and powerful data processing capabilities. The technical solution can process the captured image data in real time and quickly generate accurate measurement results to meet the fast-paced needs of industrial and commercial applications. Through adjustable beam projection and signal-to-noise ratio adjustment, this technical solution can adapt to different measurement environments and complex human body shapes, ensuring high-quality data output under various conditions.

[0112] Based on the three-dimensional human body measurement method based on 3D structured light and double-layer surface model introduced in the above embodiment, correspondingly, this application also provides a three-dimensional human body measurement device based on 3D structured light and double-layer surface model. Figure 3 Provide explanation.

[0113] Figure 3This is a schematic diagram of the structure of a three-dimensional human body measurement device based on 3D structured light and a double-layer surface model provided in an embodiment of the present application. The double-layer surface model includes an inner layer model and an outer layer model. The inner layer model is used to capture the basic shape and posture of the human body, and the outer layer model is used to capture the detailed features of non-rigid objects on the human body surface. Figure 3 As shown, the device includes:

[0114] a depth map generation module, configured to emit light having structural features to the surface of a human body to be measured through a 3D structured light camera module, capture the light reflected from the surface of the human body to be measured, and generate depth map data of the human body to be measured based on the light reflected from the surface of the human body to be measured;

[0115] A point cloud conversion module is used to establish a three-dimensional point cloud data model of the human body to be measured based on the depth map data;

[0116] a model matching and adjustment module, configured to match the three-dimensional point cloud data model with the double-layer surface model; during the matching process, adjusting the double-layer surface model based on differences between the double-layer surface model and the three-dimensional point cloud data model and a double-layer node graph of the double-layer surface model; and obtaining an adjusted double-layer surface model after the matching process is completed; the double-layer node graph comprising: in-body nodes and far-body nodes, wherein the in-body nodes correspond to the inner-layer model and the far-body nodes correspond to the outer-layer model;

[0117] The measurement data acquisition module is used to obtain three-dimensional measurement data of the human body to be measured based on the adjusted double-layer surface model.

[0118] In an optional implementation, the model matching and adjustment module is specifically configured to:

[0119] Matching the inner layer model with the three-dimensional point cloud data model to obtain an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjusting the parameters of the inner layer model based on the error;

[0120] Based on the matched inner layer model, the point cloud data in the three-dimensional point cloud data model is integrated to match the outer layer model, and the parameters of the outer layer model are adjusted during the matching process;

[0121] Synchronously binding and data fusing the matched inner layer model and the matched outer layer model, dynamically adjusting the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model, and optimizing the parameters of the double-layer surface model based on the effect of the joint motion tracking;

[0122] It is determined whether the current double-layer surface model meets the model optimization cutoff condition. If so, the adjustment of the model parameters is stopped to obtain the adjusted double-layer surface model.

[0123] In an optional implementation, the model matching and adjustment module is specifically configured to:

[0124] The parameters of the inner layer model are adjusted with the minimization of the energy function as the optimization goal; the energy function includes: a data term, a regularization term and a priori term; the data term is used to measure the difference between the inner layer model and the three-dimensional point cloud data model; the regularization term is used to constrain the degree of deformation of the inner layer model; the priori term is used to guide the convergence of the inner layer model by using prior knowledge of human body shape and posture.

[0125] In an optional implementation, the model matching and adjustment module is specifically configured to:

[0126] Controlling the rotation of the rotating device to drive the human body under test located on the rotating device to rotate synchronously; the depth map data specifically includes: a continuous depth map data stream generated based on light reflected from the surface of the human body under test during the period of synchronous rotation with the rotating device;

[0127] The double-layer surface model is controlled to rotate synchronously with the rotating device so that the double-layer surface model is matched with the corresponding target point cloud data in the three-dimensional point cloud data model at a target angle, and the parameters of the inner layer model and the parameters of the outer layer model are optimized; the target point cloud data is point cloud data converted based on the target depth map in the depth map continuous data stream; the target depth map is a depth map generated based on the light reflected from the surface when the human body to be measured is rotated to the target angle.

[0128] In an optional implementation, the model matching and adjustment module is specifically configured to:

[0129] Determining whether the matching quality of the current double-layer surface model is qualified, and determining whether the joint motion tracking effect of the current double-layer surface model is qualified;

[0130] If the matching quality is qualified and the effect of the joint motion tracking is qualified, it is determined that the current double-layer surface model meets the model optimization cutoff condition;

[0131] If the matching quality is unqualified, or the effect of the joint motion tracking is unqualified, it is determined that the current double-layer surface model does not meet the model optimization cutoff condition.

[0132] In an optional implementation, the model matching and adjustment module is specifically configured to determine whether the matching quality of the current double-layer surface model is qualified by:

[0133] If the matching error of the current double-layer surface model is less than or equal to the preset error threshold, and the surface of the current double-layer surface model is visually inspected to be smooth and consistent with the point cloud, then the matching quality of the current double-layer surface model is determined to be qualified;

[0134] If the matching error of the current double-layer surface model is greater than the preset error threshold, and a visual inspection shows that a key area of ​​the current double-layer surface model is deformed or broken, the matching quality of the current double-layer surface model is determined to be unqualified.

[0135] In an optional implementation, the model matching and adjustment module is specifically configured to determine whether the joint motion tracking effect of the current double-layer surface model is satisfactory by:

[0136] If the current dual-layer surface model is stable and always consistent with the point cloud when moving at a preset speed or performing a gesture of a preset complexity, the model is matched consistently with the point cloud across multiple frames, and the motion tracking accuracy is greater than a preset accuracy threshold, then the joint motion tracking effect of the current dual-layer surface model is considered qualified;

[0137] If the current double-layer surface model slides or misaligns when moving at a preset speed or performing a posture of preset complexity, causing the model to not match the actual posture, and if there is a freeze or distortion during the motion tracking process, then the joint motion tracking effect of the current double-layer surface model is judged to be unqualified.

[0138] In an optional implementation, the point cloud conversion module is specifically configured to:

[0139] Performing denoising and enhancement processing on the depth map data;

[0140] The denoised and enhanced depth map data is converted into a 3D point cloud data model of the human body to be measured using a 3D reconstruction algorithm.

[0141] In an optional implementation, the device further includes: a multimodal information collection module; the multimodal information collection module is configured to collect position information and / or posture information of the human body to be measured through an inertial measurement unit while the 3D structured light camera module collects light reflected from the surface of the human body to be measured;

[0142] The point cloud conversion module is specifically used to:

[0143] Using a 3D reconstruction algorithm, the denoised and enhanced depth map data is converted into preliminary point cloud data;

[0144] The position information and / or the posture information are used to provide constraints for point cloud registration, and preliminary point cloud data from multiple perspectives are registered and fused to form a three-dimensional point cloud data model of the human body to be measured.

[0145] In an optional implementation, the device also includes: a multimodal information collection module; the multimodal information collection module is used to collect the position information and / or posture information of the human body to be measured through an inertial measurement unit while the 3D structured light camera module collects the light reflected from the surface of the human body to be measured; when matching the three-dimensional point cloud data model with the double-layer surface model, the position information and / or the posture information is used to optimize the dynamic parameters of the double-layer surface model.

[0146] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0147] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A three-dimensional human body measurement method based on 3D structured light and double-layer surface model, characterized in that: The double-layer surface model includes an inner layer model and an outer layer model, wherein the inner layer model is used to capture the basic form and posture of the human body, and the outer layer model is used to capture the detailed features of non-rigid objects on the surface of the human body; The method comprises: The 3D structured light camera module emits light with structural features to the surface of the human body to be measured, captures the light reflected by the surface of the human body to be measured, and generates depth map data of the human body to be measured based on the light reflected by the surface of the human body to be measured; Establishing a three-dimensional point cloud data model of the human body to be measured according to the depth map data; Matching the three-dimensional point cloud data model with the double-layer surface model, matching the inner layer model with the three-dimensional point cloud data model, obtaining an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjusting the parameters of the inner layer model based on the error; based on the matched inner layer model, fusing the point cloud data in the three-dimensional point cloud data model to match the outer layer model, and adjusting the parameters of the outer layer model during the matching process; synchronously binding and data fusion of the matched inner layer model and the matched outer layer model, and dynamically adjusting the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model, and optimizing the parameters of the double-layer surface model based on the effect of the joint motion tracking; judging whether the current double-layer surface model meets the model optimization cutoff condition, and if so, stopping the adjustment of the model parameters to obtain the adjusted double-layer surface model; the double-layer node graph includes: in-body nodes and far-body nodes, wherein the in-body nodes correspond to the inner layer model and the far-body nodes correspond to the outer layer model; Three-dimensional measurement data of the human body to be measured is obtained according to the adjusted double-layer surface model.

2. The method according to claim 1, characterized in that The matching of the inner layer model with the three-dimensional point cloud data model to obtain an error between the inner layer model after parameter adjustment and the three-dimensional point cloud data model, and adjusting the parameters of the inner layer model based on the error, includes: The parameters of the inner layer model are adjusted with the minimization of the energy function as the optimization goal; the energy function includes: a data term, a regularization term and a priori term; the data term is used to measure the difference between the inner layer model and the three-dimensional point cloud data model; the regularization term is used to constrain the degree of deformation of the inner layer model; the priori term is used to guide the convergence of the inner layer model by using prior knowledge of human body shape and posture.

3. The method according to claim 1, characterized in that The dynamically adjusting the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model includes: Controlling the rotation of the rotating device to drive the human body under test located on the rotating device to rotate synchronously; the depth map data specifically includes: a continuous depth map data stream generated based on light reflected from the surface of the human body under test during the period of synchronous rotation with the rotating device; The double-layer surface model is controlled to rotate synchronously with the rotating device so that the double-layer surface model is matched with the corresponding target point cloud data in the three-dimensional point cloud data model at a target angle, and the parameters of the inner layer model and the parameters of the outer layer model are optimized; the target point cloud data is point cloud data converted based on the target depth map in the depth map continuous data stream; the target depth map is a depth map generated based on the light reflected from the surface when the human body to be measured is rotated to the target angle.

4. The method according to claim 1, wherein The determining whether the current double-layer surface model satisfies the model optimization cutoff condition includes: Determining whether the matching quality of the current double-layer surface model is qualified, and determining whether the joint motion tracking effect of the current double-layer surface model is qualified; If the matching quality is qualified and the effect of the joint motion tracking is qualified, it is determined that the current double-layer surface model meets the model optimization cutoff condition; If the matching quality is unqualified, or the effect of the joint motion tracking is unqualified, it is determined that the current double-layer surface model does not meet the model optimization cutoff condition.

5. The method according to claim 4, characterized in that The determining whether the matching quality of the current double-layer surface model is qualified includes: If the matching error of the current double-layer surface model is less than or equal to the preset error threshold, and the surface of the current double-layer surface model is visually inspected to be smooth and consistent with the point cloud, then the matching quality of the current double-layer surface model is determined to be qualified; If the matching error of the current double-layer surface model is greater than the preset error threshold, and a visual inspection shows that a key area of ​​the current double-layer surface model is deformed or broken, the matching quality of the current double-layer surface model is determined to be unqualified.

6. The method according to claim 4, characterized in that The determining whether the effect of the joint motion tracking of the current double-layer surface model is qualified includes: If the current dual-layer surface model is stable and always consistent with the point cloud when moving at a preset speed or performing a gesture of a preset complexity, the model is matched consistently with the point cloud across multiple frames, and the motion tracking accuracy is greater than a preset accuracy threshold, then the joint motion tracking effect of the current dual-layer surface model is considered qualified; If the current double-layer surface model slides or misaligns when moving at a preset speed or performing a posture of preset complexity, causing the model to not match the actual posture, and if there is a freeze or distortion during the motion tracking process, then the joint motion tracking effect of the current double-layer surface model is judged to be unqualified.

7. The method according to any one of claims 1 to 6, characterized in that The step of establishing a three-dimensional point cloud data model of the human body to be measured according to the depth map data comprises: Performing denoising and enhancement processing on the depth map data; The denoised and enhanced depth map data is converted into a 3D point cloud data model of the human body to be measured using a 3D reconstruction algorithm.

8. The method according to claim 7, characterized in that While the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes: Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit; The method of converting the denoised and enhanced depth map data into a three-dimensional point cloud data model of the human body to be measured by using a three-dimensional reconstruction algorithm includes: Using a 3D reconstruction algorithm, the denoised and enhanced depth map data is converted into preliminary point cloud data; The position information and / or the posture information are used to provide constraints for point cloud registration, and preliminary point cloud data from multiple perspectives are registered and fused to form a three-dimensional point cloud data model of the human body to be measured.

9. The method according to claim 1, characterized in that While the 3D structured light camera module collects light reflected from the surface of the human body to be measured, the method further includes: Collecting position information and / or posture information of the human body to be measured by an inertial measurement unit; When matching the three-dimensional point cloud data model with the double-layer surface model, the dynamic parameters of the double-layer surface model are optimized using the position information and / or the posture information.

10. A three-dimensional human body measurement device based on 3D structured light and double-layer surface model, characterized in that: The double-layer surface model includes an inner layer model and an outer layer model, wherein the inner layer model is used to capture the basic form and posture of the human body, and the outer layer model is used to capture the detailed features of non-rigid objects on the surface of the human body; The device comprises: a depth map generation module, configured to emit light having structural features to the surface of a human body to be measured through a 3D structured light camera module, capture the light reflected from the surface of the human body to be measured, and generate depth map data of the human body to be measured based on the light reflected from the surface of the human body to be measured; A point cloud conversion module is used to establish a three-dimensional point cloud data model of the human body to be measured based on the depth map data; a model matching and adjustment module, configured to match the three-dimensional point cloud data model with the double-layer surface model, match the inner layer model with the three-dimensional point cloud data model, obtain an error between the inner layer model and the three-dimensional point cloud data model after parameter adjustment, and adjust the parameters of the inner layer model based on the error; based on the matched inner layer model, fuse the point cloud data in the three-dimensional point cloud data model to match the outer layer model, and adjust the parameters of the outer layer model during the matching process; synchronously bind and fuse the matched inner layer model and the matched outer layer model, and dynamically adjust the double-layer node graph based on the motion characteristics of the three-dimensional point cloud data model to perform joint motion tracking on the double-layer surface model, and optimize the parameters of the double-layer surface model based on the effect of the joint motion tracking; determine whether the current double-layer surface model meets the model optimization cutoff condition, and if so, stop adjusting the model parameters to obtain the adjusted double-layer surface model; the double-layer node graph includes: in-body nodes and far-body nodes, wherein the in-body nodes correspond to the inner layer model and the far-body nodes correspond to the outer layer model; The measurement data acquisition module is used to obtain three-dimensional measurement data of the human body to be measured based on the adjusted double-layer surface model.

Images