A device capable of simultaneously collecting 3D information of multiple regions of an object

Abstract

This invention provides a device capable of simultaneously acquiring 3D information from multiple regions of a target object. It includes m image acquisition devices, used during the acquisition process to: acquire a first set of images of the first region of the target object by relative movement between the acquisition area of ​​the first image acquisition device and the first region of the target object; and so on, acquiring a nth set of images of the nth region of the target object by relative movement between the acquisition area of ​​the mth image acquisition device and the nth region of the target object, where m ≥ 1 and n ≥ 2; it also includes a processing unit for obtaining 3D information of the corresponding region of the target object based on multiple images in each set of images; and a measurement unit for measuring the geometric dimensions based on the 3D information of the corresponding region of the target object. This invention is the first to recognize and propose performing 3D acquisition on different regions of the same target object, thereby enabling more accurate acquisition of the target object's 3D information.

Description

Technical Field

[0001] This invention relates to the field of 3D data acquisition and measurement technology, and particularly to the field of 3D acquisition and measurement of target objects using images. Background Technology

[0002] Currently, many 3D acquisition and measurement devices are mainly designed for a specific person, a specific part of a person, an object, or a specific part of an object. For example, existing 3D devices are used to acquire 3D information about a face, an iris, or a hand. However, the information acquired in this way is relatively limited, and can only reflect one 3D feature of the target object.

[0003] Of course, some solutions utilize a single 3D acquisition device to repeatedly capture 3D information from different regions of the target object. For example, a 3D acquisition device might first capture 3D information of the face, and then capture 3D information of the iris. However, different regions of the target object have different requirements for the 3D acquisition device. For instance, face 3D acquisition requires capturing information within a 180° range around the head axis, while iris 3D acquisition only requires capturing information from a very small angle; face 3D acquisition typically uses a visible light camera, while iris 3D acquisition requires an infrared camera; face 3D acquisition and iris 3D acquisition also have different requirements for lens depth of field, lens type, etc. In other words, because different regions of the target object have different characteristics, using a single 3D acquisition device in combination will result in poor acquisition results, or even make it impossible to synthesize a 3D image.

[0004] Some other solutions claim to be able to collect information such as face and iris simultaneously and have set up multiple acquisition cameras, but in reality, none of them have designed special acquisition camera devices specifically for face and iris. In essence, they are the same as the above solutions, which also use general acquisition devices for acquisition.

[0005] Some methods can collect data on different targets of varying sizes. However, these methods collect data on different targets, not different areas within the same target. Collecting data on different targets requires changing the target or moving the data collection equipment during the process. Collecting data on different areas of the same target, on the other hand, eliminates the need to change the target or move the equipment, making it much more convenient.

[0006] In addition, there are some traditional solutions that can simultaneously acquire 2D information from the face and iris. However, since 2D information is essentially acquired from a single image, while 3D information involves multiple image acquisitions, selection of suitable images, image stitching, and 3D synthesis, the requirements are very different from those of 2D acquisition (for example, 2D acquisition does not need to consider the acquisition range, only that the image includes the required area; while the 3D face acquisition range is 180°, and the 3D iris acquisition only needs 15°-30°). Therefore, solutions that are easy to conceive and solve in 2D acquisition are not easy to conceive and solve in 3D acquisition, and cannot be simply copied or combined.

[0007] Currently, there is no technical solution for collecting 3D information of the same target object from different regions based on the 3D characteristics of different regions. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide an apparatus capable of simultaneously acquiring 3D information from multiple regions of a target object, which overcomes or at least partially solves the above problems.

[0009] This invention provides a device capable of simultaneously acquiring 3D information from multiple regions of a target object, comprising m image acquisition devices for use during the acquisition process:

[0010] The first image acquisition device moves relative to the first region of the target object, and acquires the first set of images of the first region of the target object;

[0011] Similarly, by moving the acquisition area of ​​the m-th image acquisition device relative to the n-th region of the target object, the n-th group of images of the n-th region of the target object is acquired, where m≥1 and n≥2;

[0012] It also includes a processing unit, used to obtain 3D information of the region corresponding to the target object based on multiple images in each group of images;

[0013] It also includes a measurement unit for measuring geometric dimensions based on 3D information of the corresponding region of the target object;

[0014] During the relative motion, when each image acquisition device acquires an image, the three adjacent positions satisfy the condition that the three images acquired at the corresponding positions all contain at least a portion representing the same area of ​​the target object.

[0015] Optionally, at least two of the regions may include overlapping portions.

[0016] Optionally, at least two of the regions do not include overlapping portions.

[0017] Optionally, the relative motion is generated by the relative motion between the image acquisition device and the target object, or the relative motion is generated by optical scanning of the image acquisition device.

[0018] Optionally, multiple image acquisition devices may be mounted on the motion device.

[0019] Optionally, each image acquisition device can be mounted on a different motion device.

[0020] Optionally, during the relative motion, the two adjacent positions of each image acquisition device when acquiring an image must satisfy at least the following conditions:

[0021] H*(1-cosb)=L*sin2b;

[0022] a = m * b;

[0023] 0 <m<0.8;

[0024] Where L is the distance from the image acquisition device to the target object, H is the actual size of the target object in the acquired image, a is the angle between the optical axes of the image acquisition devices at two adjacent positions, and m is a coefficient.

[0025] Optionally, depending on the different characteristics of different regions of the target object, the corresponding image acquisition device may have different optical properties.

[0026] Optionally, the different features of different regions of the target object include one or more of the following: region size, distance of the target object region from the corresponding image acquisition device, and longitudinal depth of the region.

[0027] The present invention also provides a multi-region 3D information comparison device or a target object accessory generation device, including any of the devices described in any one of them; or generating an accessory that matches the corresponding region of the target object using at least one region 3D information obtained by any one of the devices described in any one of them.

[0028] Invention points and technical effects

[0029] 1. It was the first time that 3D acquisition of different regions of the same target object was proposed, so as to obtain the 3D information of the target object more accurately.

[0030] 2. Design dedicated 3D acquisition units for different regions of the same target object to achieve more accurate acquisition.

[0031] 3. Using guide rails or rotating shafts to drive the camera can avoid using too many cameras, saving costs and reducing size.

[0032] 4. By using multiple dedicated 3D acquisition units corresponding to different areas, multiple 3D information of the target object can be acquired at once, improving the acquisition speed.

[0033] 5. By utilizing the rotation-reset process of the acquisition unit, two areas can be acquired at once when using a single 3D acquisition unit, resulting in higher acquisition efficiency. Attached Figure Description

[0034] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0035] Figure 1 A schematic diagram of a specific embodiment of a multi-region 3D information acquisition device according to an embodiment of the present invention is shown;

[0036] Figure 2 A schematic diagram of a specific embodiment of the multi-region 3D information acquisition device using a single track according to the present invention is shown;

[0037] Figure 3 A schematic diagram of a specific embodiment of a multi-region 3D information acquisition device employing an optical scanning device according to the present invention is shown;

[0038] Explanation of reference numerals in the attached figures:

[0039] 101 Face track, 102 Iris track, 103 Track, 201 Face image acquisition unit, 202 Iris image acquisition unit, 2011 Face camera, 2021 Iris camera, 2012 Face motion platform, 2022 Iris motion platform, 100 Processing unit, 401 Face optical scanning device, 402 Iris optical scanning device, 4011 Face light deflection unit, 4021 Iris light deflection unit. Detailed Implementation

[0040] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0041] Example 1

[0042] To address the aforementioned technical problems, one embodiment of the present invention provides a device capable of simultaneously acquiring 3D information from multiple regions of a target object. Please refer to... Figure 1As shown, it specifically includes: a face track 101, an iris track 102, a face image acquisition unit 201, an iris image acquisition unit 202, and a processing unit 100. It also includes a servo motor (not shown in the figure) that can drive the face image acquisition unit 201 and the iris image acquisition unit 202 to move on their respective tracks 101 and 102.

[0043] The face image acquisition unit 201 includes a face camera 2011, and in some cases may also include a face motion platform 2012, wherein the motion platform can be a one-axis, two-axis, three-axis, four-axis, five-axis, or six-axis motion platform. The motion platform can drive the face camera 2011 to achieve translation and / or rotation functions. The iris image acquisition unit 202 includes an iris camera 2021, and in some cases may also include an iris motion platform 2022, wherein the motion platform can be a one-axis, two-axis, three-axis, four-axis, five-axis, or six-axis motion platform. The motion platform can drive the iris camera 2021 to achieve translation and / or rotation functions.

[0044] The processing unit 100 controls the corresponding servo motors to drive the face image acquisition unit 201 and the iris image acquisition unit 202 to move along their respective tracks 101 and 102. This allows the face camera 2011 to rotate 180° around the human head, thereby capturing multiple images of the human head; and the iris camera 2021 to rotate 90° around the human eye, thereby capturing multiple images of the human iris. Depending on the actual 3D acquisition needs, the face camera 2011 can also rotate around the human head at any angle, such as 45°, 90°, 270°, or 360°. Furthermore, depending on the iris acquisition requirements, it can acquire iris information from one eye or both eyes. If only one eye is being acquired, a rotation of approximately 20° is sufficient. It is understood that the required camera rotation angle is related to the size of the target area, the distance between the camera and the target area, and the camera's focal length. These parameters can be pre-input, and the processing unit 100 calculates and controls the corresponding camera rotation angle. Additionally, based on the characteristics of the data collection area, start and end points can be identified, and the camera can be controlled to take pictures between these points. For example, the location of the corner of the eye can be identified, and a picture can be taken when the camera moves to that location and stop when it moves away from the other corner of the eye. Furthermore, the timing of camera shots can be left uncontrolled, starting at the beginning of the track and stopping at the end.

[0045] The processing unit 100 receives a set of images from the face camera 2011 and the iris camera 2021, and selects multiple images of the face and multiple images of the iris from the image set. Then, it synthesizes a 3D facial image using the multiple facial images and a 3D iris image using the multiple iris images. The synthesis method can use image stitching based on feature points of adjacent images, or other methods.

[0046] Multi-region 3D information acquisition method:

[0047] The required camera acquisition range is determined based on the location, lateral dimensions, and depth of the target area. For example, when acquiring 3D information of a face and iris, the acquisition angle range for a face is 0-180°, while the acquisition range for a single iris is approximately 80°-100°. Combining these ranges with parameters such as the distance between the camera and the target area and the camera's focal length, the actual angle the camera needs to rotate is determined.

[0048] The processing unit 100 controls the corresponding servo motors to drive the face image acquisition unit 201 and the iris image acquisition unit 202 to move along their respective tracks 101 and 102, allowing the face camera 2011 to rotate 180° around the human head and the iris camera 2021 to rotate 90° around the human eyes. Simultaneously, during this movement, the processing unit 100 controls the shutters of the face camera 2011 and the iris camera 2021 to capture multiple images of the human head and multiple images of the human iris. Tracks 101 and 102 are arranged vertically in parallel.

[0049] The aforementioned rotation and shutter can be performed simultaneously, meaning that the shutter can be controlled to take a picture without interrupting the rotation of the face image acquisition unit 201 or the iris image acquisition unit 202.

[0050] Alternatively, the face image acquisition unit 201 or the iris image acquisition unit 202 can be rotated to a certain position and then stopped to take a picture. After taking the picture, the rotation process can be resumed. That is, the shutter control can be interrupted to take pictures continuously during the rotation process.

[0051] The processing unit 100 receives two sets of images sent by the face image 2011 and the iris image 2021 from the camera, and selects multiple images of the face and multiple images of the iris from the face image set and the iris image set, respectively.

[0052] Then, multiple images of the face are used to synthesize a 3D facial image, and multiple images of the iris are used to synthesize a 3D iris image.

[0053] The synthesis method can use image stitching based on the feature points of adjacent images, or it can use other methods.

[0054] Image stitching methods include:

[0055] (1) Process multiple images separately and extract their respective feature points; the features of each feature point in multiple images can be described using SIFT (Scale-Invariant Feature Transform) feature descriptors. SIFT feature descriptors have 128 feature description vectors, which can describe 128 aspects of any feature point in terms of direction and scale, significantly improving the accuracy of feature description, while the feature descriptors are spatially independent.

[0056] (2) Based on the extracted feature points from multiple images, feature point cloud data for facial features and feature point cloud data for iris features are generated respectively. Specifically, this includes:

[0057] (2-1) Based on the features of each feature point in the extracted multiple images, feature points of multiple images are matched to establish a matching facial feature point dataset; based on the features of each feature point in the extracted multiple images 3012, feature points of multiple images are matched to establish a matching iris feature point dataset.

[0058] (2-2) Based on the camera's optical information and the different camera positions when acquiring multiple images, calculate the relative spatial position of the camera with respect to the feature points at each position, and calculate the spatial depth information of the feature points in multiple images based on the relative positions. Similarly, the spatial depth information of the feature points in multiple images can be calculated. The calculation can be performed using bundle adjustment.

[0059] Calculating the spatial depth information of feature points can include spatial location information and color information. Specifically, this can include the X-axis coordinates, Y-axis coordinates, and Z-axis coordinates of the feature point, as well as the R-channel, G-channel, B-channel, and alpha-channel values ​​of the feature point's color information. Thus, the generated feature point cloud data contains both spatial location and color information of the feature points. The format of the feature point cloud data can be as follows:

[0060] X1 Y1 Z1 R1 G1 B1 A1

[0061] X2 Y2 Z2 R2 G2 B2 A2

[0062] ...

[0063] Xn Yn Zn Rn Gn Bn An

[0064] Where Xn represents the X-axis coordinate of the feature point in space; Yn represents the Y-axis coordinate of the feature point in space; Zn represents the Z-axis coordinate of the feature point in space; Rn represents the R-channel value of the feature point's color information; Gn represents the G-channel value of the feature point's color information; Bn represents the B-channel value of the feature point's color information; and An represents the Alpha channel value of the feature point's color information.

[0065] (2-3) Generate feature point cloud data of face and iris features based on feature point datasets and spatial depth information of feature points from multiple image matching.

[0066] (2-4) Construct 3D models of the face and iris based on feature point cloud data to achieve the acquisition of 3D point cloud data of the face and iris.

[0067] (2-5) The collected target object color and texture are added to the point cloud data to form a 3D image of the face and iris.

[0068] One approach is to synthesize a 3D image using all images in a set of images, or to select high-quality images from the set for synthesis.

[0069] The above stitching methods are just limited examples and are not limited to them. All methods for generating three-dimensional images from multiple two-dimensional images from multiple angles can be used.

[0070] In the above method, both the face image acquisition unit 201 and the iris image acquisition unit 202 use a single camera to capture multiple images from different angles through relative movement with the face and iris.

[0071] The acquisition position during relative motion is determined by the position of the image acquisition device 201 when acquiring the image of the target object, and the two adjacent positions must at least satisfy the following conditions:

[0072] H*(1-cosb)=L*sin2b;

[0073] a = m * b;

[0074] 0 <m<1.5;

[0075] Where L is the distance from the image acquisition device to the target object, which is usually the distance from the image acquisition device to the area directly opposite the target object when it is in the first position, and m is a coefficient.

[0076] H represents the actual size of the target object in the acquired image. The image is typically a picture taken by the image acquisition device 201 at the first position. The target object in this image has actual geometric dimensions (not the dimensions shown in the image). This dimension is measured along the direction from the first position to the second position. For example, if the first and second positions are in a horizontal relationship, then the dimension is measured along the horizontal direction of the target object. For example, if the leftmost end of the target object visible in the image is A and the rightmost end is B, then the straight-line distance from A to B on the target object is measured as H. The measurement method can be based on the distance between A and B in the image, combined with the focal length of the camera lens, to calculate the actual distance. Alternatively, A and B can be marked on the target object, and the straight-line distance between A and B can be directly measured using other measurement methods.

[0077] 'a' represents the angle between the optical axes of two adjacent image acquisition devices.

[0078] m is a coefficient.

[0079] Because objects vary in size and surface texture, the value of 'a' cannot be strictly defined by a formula and must be determined empirically. Based on numerous experiments, a value of 'm' within 1.5 is acceptable, but preferably within 0.8. Specific experimental data are shown in the table below:

[0080] Human head 0.1、0.2、0.3、0.4 very good >90％ Human head 0.5、0.6 good >85％ Human head 0.7、0.8 Better >80％ Human head 0.9、1.0 generally >70％ Human head 1.0、1.1、1.2 generally >60％ Human head 1.2、1.3、1.4、1.5 Barely synthesized >50％ Human head 1.6、1.7 Difficult to synthesize <40％

[0081] After the target object and image acquisition device 201 are determined, the value of 'a' can be calculated according to the above empirical formula. Based on the value of 'a', the parameters of the virtual matrix, i.e. the positional relationship between matrix points, can be determined.

[0082] In general, a virtual matrix is ​​a one-dimensional matrix, for example, multiple matrix points (collection positions) are arranged along the horizontal direction. However, when the target object is large, a two-dimensional matrix is ​​required, in which case two adjacent positions in the vertical direction also satisfy the above-mentioned 'a' value condition.

[0083] In some cases, even using the above empirical formula, it is not easy to determine the value of 'a'. In such cases, it is necessary to adjust the matrix parameters experimentally. The experimental method is as follows: Calculate the prediction matrix parameter 'a' according to the above formula, and control the camera to move to the corresponding matrix point according to the matrix parameter. For example, the camera takes image P1 at position W1, moves to position W2, and takes image P2. Then compare whether there is a part representing the same area of ​​the target object in images P1 and P2, i.e., P1∩P2 is not empty (e.g., both contain the corner of the human eye, but the shooting angles are different). If not, readjust the value of 'a', move back to position W2', and repeat the above comparison steps. If P1∩P2 is not empty, continue to move the camera to position W3 according to the value of 'a' (adjusted or unadjusted), and take image P3. Again, compare whether there is a part representing the same area of ​​the target object in images P1, P2, and P3, i.e., P1∩P2∩P3 is not empty. Then, use multiple images to synthesize a 3D image and test the 3D synthesis effect. If it meets the requirements for 3D information acquisition and measurement, it is acceptable. In other words, the structure of the matrix is ​​determined by the position of the image acquisition device 201 when acquiring multiple images, and the three adjacent positions satisfy that the three images acquired at the corresponding positions all contain at least a part representing the same area of ​​the target object.

[0084] Existing technologies primarily improve 3D compositing effects through hardware upgrades and rigorous calibration. However, existing technologies lack any insights into how to optimize camera angle and position during image capture to guarantee the quality and stability of 3D compositing, and offer no specific optimization conditions. This invention, for the first time, proposes optimizing camera angle and position to ensure the quality and stability of 3D compositing. Through repeated experiments, it proposes optimal empirical conditions for camera position (as mentioned above), significantly improving the quality and stability of the 3D compositing image. This is one of the key inventive aspects of this invention.

[0085] The above embodiments can also include a third track and a third image acquisition unit, a fourth track and a fourth image acquisition unit, etc., for acquiring 3D information of the nose, teeth, etc. The specific structure and control method are similar to those described above and will not be repeated. It is understood that the number of tracks and acquisition units can be set as needed to acquire different areas. Furthermore, the tracks can be straight or curved, selected based on the general outline of the target object's area to be measured.

[0086] Because different parts of the human body have different 3D characteristics, different cameras are required. For example, the human face has many concavities and convexities, so the camera needs to have a certain depth of field to ensure that the captured image is clear; the iris requires an infrared camera to avoid interference and better reflect the 3D information of the iris; at the same time, the iris has much less concavity and convexity than the face, so a macro lens is needed to obtain a clearer image.

[0087] (1) Zoom

[0088] After the camera captures the target object, the proportion of the target object in the camera frame is estimated and compared with a predetermined value. If it is too large or too small, zooming is required. The zooming method can be: using an additional displacement device to move the image acquisition device radially, so that the image acquisition device can move closer to or further away from the target object, thereby ensuring that the proportion of the target object in the frame remains basically constant at each matrix point.

[0089] It also includes a ranging device that can measure the real-time distance (object distance) between the image acquisition device and the object. The relationship between the object distance, the proportion of the target object in the frame, and the focal length can be tabulated. Based on the focal length and the proportion of the target object in the frame, the appropriate object distance can be determined by looking up the table, thereby determining the camera position.

[0090] In some cases, when the target object or the area of ​​the target object changes relative to the camera at different matrix points, the proportion of the target object in the frame can be kept constant by adjusting the focal length.

[0091] Meanwhile, some objects have significant depth differences in different areas, such as a woman's braid, which protrudes noticeably from her head. Directly shooting such an object would require a very high depth of field from the camera (this was the first time the applicant noticed this problem). In this case, the processing unit 100 controls the motion platforms 2012 and 2022. When a certain area of ​​the target object protrudes relative to the camera, the motion platform moves the camera away from the target object; when a certain area of ​​the target object is recessed relative to the camera, the motion platform moves the camera closer to the target object, thus keeping the distance between the camera and different target areas of the human body essentially constant. This is one of the inventive points of this invention.

[0092] (2) Autofocus

[0093] During 3D image acquisition, the ranging device measures the distance (object distance) h(x) between the camera and the object in real time and sends the measurement result to the processing unit 100. The processing unit 100 looks up the object distance-focal length table, finds the corresponding focal length value, sends a focus signal to the camera, and controls the camera's ultrasonic motor to drive the lens movement for rapid focusing. In this way, rapid focusing can be achieved without adjusting the position of the image acquisition device or significantly adjusting its lens focal length, ensuring that the images captured by the image acquisition device are clear. This is one of the inventive points of this invention. Of course, in addition to focusing via ranging, focusing can also be achieved through image contrast comparison.

[0094] In existing systems, camera focusing can only occur at the initial stage, with the camera taking a series of photos at a fixed focal length throughout the rotation. This can lead to blurry images when dealing with objects with significant unevenness. Current systems are unaware of this problem when 3D capturing objects with large surface areas, and have not attempted to solve it. The main reason is that current cameras with automatic optical focusing complete focusing before shooting, achieved by lightly pressing the shutter button. It's difficult to focus while rotating, a limitation inherent to the camera's control method. Current cameras are designed for capturing 2D images and do not require frequent focusing; their autofocus relies on the shutter button, lacking protocols and / or interfaces for external software control. Furthermore, current focusing methods require sophisticated focusing strategies due to the uncertainty of the target object, resulting in slow speeds that negatively impact user experience and are unsuitable for 3D capture. The ranging device measures the distance (object distance) h(x) between the camera and the object in real time and sends the measurement result to the processing unit 100. The processing unit 100 looks up the object distance-focal length table, finds the corresponding focal length value, sends a focus signal to the camera, and controls the camera's ultrasonic motor to drive the lens movement for rapid focusing. This allows for rapid focusing without adjusting the position of the image acquisition device or significantly adjusting its lens focal length, ensuring clear images. This is one of the inventive points of this invention. Of course, besides focusing via ranging, image contrast comparison can also be used. This system sends a focus start signal directly to the camera processing unit 100 through external software, activating the internal focusing program of the processing unit 100, thereby enabling the face camera 2011 and iris camera 2021 to focus. Multiple automatic focusing attempts can be performed during the rotation of the face camera 2011 and iris camera 2021, ensuring clear images. This is also one of the inventive points of this invention. Furthermore, this invention optimizes the focusing strategy based on the relatively definite characteristics of the target object, resulting in faster focusing speeds to meet the needs of 3D acquisition.

[0095] (3) Camera and lens selection

[0096] The optical parameters of the camera can be selected based on one or more of the following: the characteristics of the target object, the size of the area to be acquired, the distance of the target area from the corresponding image acquisition device, and the vertical depth of the area.

[0097] For example, iris scans are best captured using infrared lenses and cameras, and due to their small size, macro lenses, such as those with a focal length of 100mm, are suitable. Face scans use lenses with a focal length of 20mm, while furniture scans use lenses with focal lengths of 18–55mm.

[0098] Example 2

[0099] Under the premise of other similar structures, please refer to Figure 2 As shown, only one track 103 can be set up, with both the face image acquisition unit 201 and the iris image acquisition unit 202 mounted on track 103. The face image acquisition unit 201 and the iris image acquisition unit 202 can rotate sequentially along the track. For example, the face image acquisition unit 201 and the iris image acquisition unit 202 can be arranged side-by-side, rotating sequentially from left to right along the track. Upon reset, the iris image acquisition unit 202 and the face image acquisition unit 201 can rotate sequentially from right to left along the track. Alternatively, the face image acquisition unit 201 and the iris image acquisition unit 202 can be arranged vertically side-by-side, rotating simultaneously from left to right along the track.

[0100] Example 3

[0101] Under similar structural conditions, only one track 103 and one image acquisition unit 203 can be set up. This acquisition unit can acquire images of the target area. For example, to acquire 3D information of two hands (different areas of the same target), both hands can be placed in the scanning area, and the image acquisition unit can rotate along the track to take multiple pictures of each hand in sequence. Then, the processing unit 100 can synthesize the 3D images of the left and right hands respectively. A similar method can be used for the irises of both eyes. A similar method can also be used for the two handles of an antique vase.

[0102] Example 4

[0103] Under similar structural conditions, a rotating device can replace the track. The image acquisition unit is mounted on the rotating device, and the processing unit 100 can control the rotation of the rotating device to drive the image acquisition unit to rotate, allowing the image acquisition device to acquire multiple images of a specific area of ​​the target object from different angles. For example, the rotating device drives the image acquisition device to rotate along the left eye to acquire multiple images of the left eye, and continues rotating to acquire multiple images of the right eye. After transmitting these two sets of images to the processing unit 100, the processing unit 100 synthesizes a 3D image of the left iris and a 3D image of the right iris, respectively. Similarly, 3D images of the left foot, right foot, left hand, and right hand can be acquired. Because this method does not require a track, it is smaller in size. This is also one of the invention's key features.

[0104] Example 5

[0105] Under similar structural conditions, the acquisition device can use a robotic arm instead of a track. That is, the image acquisition unit is mounted on the robotic arm, and multiple robotic arms are controlled to drive multiple cameras to capture images of different areas of the object.

[0106] Example 6

[0107] Please refer to Figure 3 As shown, the device includes a face image acquisition unit 201, an iris image acquisition unit 202, a face optical scanning device 401, and an iris optical scanning device 402, such that the acquisition areas of the image acquisition devices 201 and 202 move relative to the target object without moving or rotating.

[0108] The face optical scanning device 401 and the iris optical scanning device 402 also include a face light deflection unit 4011 and an iris light deflection unit 4021. Optionally, the light deflection units are driven by a light deflection driving unit. The physical position of the image acquisition device does not change, i.e., it does not move or rotate. The light deflection units cause a certain change in the camera's acquisition area, so that the target object and the acquisition area change. During this process, the light deflection units 4011 and 4021 can be driven by the light deflection driving unit to allow light from different directions to enter the image acquisition device. The light deflection driving unit can be a drive device that controls the linear motion or rotation of the light deflection units 4011 and 4021. Both the light deflection driving unit and the camera are connected to a control terminal, which is used to control the rotating shaft drive device to perform driving and the camera to capture images.

[0109] The control terminal can be a processing unit, a computer, a remote control center, etc.

[0110] The image acquisition device can be replaced with other image acquisition devices such as cameras, CCDs, and infrared cameras. Meanwhile, the image acquisition device is fixed to the mounting platform, and its position remains unchanged.

[0111] The light deflection drive unit can be selected from brushless motors, high-precision stepper motors, angle encoders, rotary motors, etc.

[0112] The light deflection units 4011 and 4021 are reflectors. It can be understood that one or more reflectors can be set according to the measurement needs, and one or more light deflection drive units can be set accordingly, and the angle of the plane mirror can be controlled to change so that light from different directions enters the image acquisition device.

[0113] The light deflection units 4011 and 4021 are lens groups. The lens group can be set to one or more lenses. The light deflection drive unit can be set to one or more accordingly, and control the plane mirror angle to change so that light from different directions enters the image acquisition device.

[0114] The light deflection units 4011 and 4021 include a rotating body and multiple light deflection sub-lenses of different specifications located on the rotating body. The rotation axis of the rotating body is parallel to and offset from the central axis of the lens of the image acquisition device by a certain distance, so that light rays from different directions enter the image acquisition device after passing through the circumferential light deflection sub-lenses located at the front end of the rotating body. The light deflection drive unit controls the rotation of the rotating body. It can be understood that the light deflection sub-lenses of different specifications can be replaced with reflectors with different minute angle changes.

[0115] The light deflection units 4011 and 4021 include multi-faceted rotating mirrors.

[0116] In this invention, the target object can be a single solid object or a combination of multiple objects.

[0117] The 3D information of the target object includes 3D images, 3D point clouds, 3D meshes, local 3D features, 3D dimensions, and all parameters that contain the 3D features of the target object.

[0118] In this invention, 3D or three-dimensional refers to information in three directions (X, Y, and Z), especially depth information, which is fundamentally different from information that only has two-dimensional planar dimensions. It is also fundamentally different from some definitions that use terms like 3D, panoramic, holographic, or three-dimensional, but actually only include two-dimensional information, particularly excluding depth information.

[0119] The acquisition area referred to in this invention is the range that an image acquisition device (such as a camera) can capture.

[0120] The image acquisition device in this invention can be a CCD, CMOS, camera, camcorder, industrial camera, monitor, webcam, mobile phone, tablet, laptop, mobile terminal, wearable device, smart glasses, smartwatch, smart bracelet, and all devices with image acquisition function.

[0121] The above embodiments all use the image acquisition unit to move to obtain multiple images from different angles. It can be understood that the target object can also be moved or rotated to obtain images of multiple areas of the target object from different angles, thereby generating 3D images of multiple areas of the target object.

[0122] The solutions described above can also be used to acquire 3D images of more regions. For example, by adding tracks, 3D images of the face, iris, nose, and teeth can be acquired simultaneously, as can 3D images of the entire human body, hands, feet, head, and abdomen. In other words, the solution of this invention can acquire 3D information of multiple regions of the same target object. These regions can be in a relationship of inclusion (e.g., face and nose; the entire human body and head), an overlapping relationship (e.g., the overlapping front and base of the palm), or an independent relationship (e.g., left and right hands). Overlapping and inclusion relationships can be collectively referred to as overlapping relationships. In other words, this invention can acquire 3D information of a target object from the whole or from multiple parts, making the information acquisition more comprehensive and accurate. This results in greater accuracy in subsequent comparisons or design using 3D information.

[0123] You can use one camera to photograph one area, or one camera to photograph multiple areas, or you can combine the two methods to form a correspondence between m cameras and n areas.

[0124] The 3D information of multiple regions of the target object obtained in the above embodiments can be used for comparison, such as for identity recognition. First, the 3D information of the human face and iris is acquired using the scheme of this invention and stored in a server as standard data. When needed, such as for identity authentication for payment or door opening, the 3D acquisition device can again collect and acquire the 3D information of the human face and iris, compare it with the standard data, and if the comparison is successful, the next step can be performed. It can be understood that this comparison can also be used for the authentication of fixed assets such as antiques and works of art. That is, 3D information of multiple regions of the antique or work of art is first acquired as standard data, and when authentication is required, the 3D information of multiple regions is acquired again and compared with the standard data to determine authenticity.

[0125] The 3D information of multiple regions of the target object obtained in the above embodiments can be used to design, produce, and manufacture accessories for the target object. For example, obtaining 3D data of a human head can help design and manufacture a more suitable hat; obtaining data of a human head and 3D data of the eyes can help design and manufacture suitable glasses.

[0126] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0127] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0128] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0129] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0130] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the visible light camera-based four-dimensional biometric data acquisition device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0131] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0132] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. An apparatus capable of simultaneous 3D information acquisition of multiple regions of an object, characterized in that: Includes m image acquisition devices, used during the acquisition process: The first image acquisition device moves relative to the first region of the target object, and acquires the first set of images of the first region of the target object; Similarly, by moving the acquisition area of ​​the m-th image acquisition device relative to the n-th region of the target object, the n-th group of images of the n-th region of the target object is acquired, where m≥1 and n≥2; It also includes a processing unit, used to obtain 3D information of the region corresponding to the target object based on multiple images in each group of images; It also includes a measurement unit for measuring geometric dimensions based on 3D information of the corresponding region of the target object; During the relative motion, when each image acquisition device acquires an image, the three adjacent positions satisfy the condition that the three images acquired at the corresponding positions all contain at least a portion representing the same area of ​​the target object; During the relative motion, the two adjacent positions of each image acquisition device when acquiring an image must at least satisfy the following conditions: H*(1-cosb)=L*sin2b; a = m * b; 0<m<0.8； Where L is the distance from the image acquisition device to the target object, H is the actual size of the target object in the acquired image, a is the optical axis angle between two adjacent image acquisition devices, and m is a coefficient; Specifically, the matrix parameter 'a' is predicted, and the camera is moved to the corresponding matrix point according to the matrix parameter. For example, the camera takes image P1 at position W1, and then takes image P2 after moving to position W2. At this point, it is compared whether there is a part representing the same area of ​​the target object in images P1 and P2, i.e., P1∩P2 is not empty. If not, the value of 'a' is readjusted, and the camera is moved back to position W2', and the above comparison steps are repeated. If P1∩P2 is not empty, the camera is moved to position W3 according to the value of 'a', and image P3 is taken. The comparison is repeated again to see whether there is a part representing the same area of ​​the target object in images P1, P2, and P3, i.e., P1∩P2∩P3 is not empty. Then, multiple images are used to synthesize a 3D image, and the 3D synthesis effect is tested. If it meets the requirements for 3D information acquisition and measurement, it is considered successful.

2. The apparatus of claim 1, wherein: At least two of the regions include overlapping portions.

3. The apparatus of claim 1, wherein: At least two of the regions do not include overlapping portions.

4. The apparatus of claim 1, wherein: The relative motion is generated by the relative motion between the image acquisition device and the target object, or the relative motion is generated by the optical scanning of the image acquisition device.

5. The apparatus of claim 1, wherein: Multiple image acquisition devices are installed on the motion device.

6. The apparatus of claim 5, wherein: Each image acquisition device is mounted on a different motion device.

7. The apparatus of claim 1, wherein: Depending on the different characteristics of different regions of the target object, the corresponding image acquisition device has different optical properties.

8. The apparatus of claim 7, wherein: The different characteristics of different regions of the target object include one or more of the following: region size, distance of the target object region from the corresponding image acquisition device, and longitudinal depth of the region.

9. A multi-region 3D information matching device or a kit for a target object, characterized by: Includes the apparatus according to any one of claims 1-8; or generates a matching item that matches the corresponding area of ​​the target object using at least one region 3D information obtained by the apparatus according to any one of claims 1-8.

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