CN111028341B - Three-dimensional model generation method - Google Patents
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Abstract
The invention provides a three-dimensional model generation method, which comprises the following steps: the first step is as follows: acquiring a plurality of groups of images of a target object by using 3D information acquisition equipment; the second step is that: performing image enhancement processing on all input photos; the third step: extracting characteristic points of all input images, and matching the characteristic points to obtain sparse characteristic points; the fourth step: inputting matched feature point coordinates, and resolving the sparse human face three-dimensional point cloud and the position and posture data of the image acquisition device to obtain a sparse target object model three-dimensional point cloud and model coordinate values of the position; taking the sparse feature points as initial values, performing multi-view image dense matching, and obtaining dense point cloud data; the fifth step: reconstructing a curved surface of the target object by using the dense point cloud; and a sixth step: and carrying out texture mapping on the target object model. The method is firstly proposed to improve the synthesis speed and the synthesis precision by increasing the mode that the background plate rotates along with the camera and matching with the optimized algorithm.
Description
Technical Field
The invention relates to the technical field of topography measurement, in particular to the technical field of 3D topography measurement.
Background
When performing 3D measurement, processing and manufacturing by using 3D measurement data, or displaying and identifying by using 3D data, a more accurate 3D model should be established for a target object first. The currently common method includes using a machine vision mode to collect pictures of an object from different angles, and matching and splicing the pictures to form a 3D model. When pictures at different angles are collected, a plurality of cameras can be arranged at different angles of the object to be detected, and the pictures can be collected from different angles through rotation of a single camera or a plurality of cameras. However, both of these methods involve problems of synthesis speed and synthesis accuracy. The synthesis speed and the synthesis precision are a pair of contradictions to some extent, and the improvement of the synthesis speed can cause the final reduction of the 3D synthesis precision; to improve the 3D synthesis accuracy, the synthesis speed needs to be reduced, and more pictures need to be synthesized.
First, there is no algorithm capable of improving the synthesis speed and the synthesis effect at the same time in the prior art.
Secondly, the collection and synthesis are generally considered to be two processes, which do not affect each other and are not considered uniformly. This affects the efficiency of 3D synthesis modeling and does not compromise the improvement of synthesis speed and synthesis accuracy.
Finally, in the prior art, it has also been proposed to use empirical formulas including rotation angle, object size, and object distance to define the camera position, thereby taking into account the speed and effect of the synthesis. However, in practical applications it is found that: unless a precise angle measuring device is provided, the user is insensitive to the angle and is difficult to accurately determine the angle; the size of the target is difficult to accurately determine, and particularly, the target needs to be frequently replaced in certain application occasions, each measurement brings a large amount of extra workload, and professional equipment is needed to accurately measure irregular targets. The measured error causes the camera position setting error, thereby influencing the acquisition and synthesis speed and effect; accuracy and speed need to be further improved.
Therefore, the technical problems that ① can simultaneously improve the synthesis speed and the synthesis precision through an algorithm, and ② can be matched with an image acquisition method so as to simultaneously improve the synthesis speed and the synthesis precision are urgently needed to be solved.
Disclosure of Invention
In view of the above, the present invention has been developed to provide a three-dimensional model generation method that overcomes, or at least partially solves, the above-mentioned problems.
The invention provides a three-dimensional model generation method and equipment, which comprise the following steps:
the first step is as follows: acquiring a plurality of groups of images of a target object by using 3D information acquisition equipment;
the second step is that: performing image enhancement processing on all input photos;
the third step: extracting characteristic points of all input images, and matching the characteristic points to obtain sparse characteristic points;
the fourth step: inputting matched feature point coordinates, and resolving the sparse human face three-dimensional point cloud and the position and posture data of the image acquisition device to obtain a sparse target object model three-dimensional point cloud and model coordinate values of the position; taking the sparse feature points as initial values, performing multi-view image dense matching, and obtaining dense point cloud data;
the fifth step: reconstructing a curved surface of the target object by using the dense point cloud;
and a sixth step: and carrying out texture mapping on the target object model.
Optionally, the first step of collecting equipment includes an image collecting device, a rotating device and a background plate.
Optionally, two adjacent acquisition positions of the image acquisition device in the first step satisfy the following conditions:
wherein L is the linear distance of the optical center of the image acquisition device when two adjacent acquisition positions are located, f is the focal length of the image acquisition device, d is the rectangular length or width of the photosensitive element (CCD) of the image acquisition device, T is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis, and T is an adjustment coefficient of < 0.603.
Optionally, the image enhancement processing in the second step includes:
the contrast of the original picture is enhanced and the noise is suppressed at the same time by adopting the following filter;
in the formula: g (x, y) is the gray value of the original image at (x, y), f (x, y) is the gray value of the original image at the position after being enhanced by the Wallis filter, and mgIs the local gray average value, s, of the original imagegIs the local standard deviation of gray scale of the original image, mfFor the transformed image local gray scale target value, sfFor the transformed image local gray standard deviation target value, c ∈ (0, 1) is the spreading constant of the image variance, and b ∈ (0, 1) is the image luminance coefficient constant.
Optionally, the third step includes ① constructing a Hessian matrix to generate all interest points for feature extraction, ② constructing scale space feature point positioning, ③ determining the principal direction of the feature points, ④ generating 64-dimensional feature point description vectors, and ⑤ feature point matching.
Optionally, when the Hessian matrix is used to detect the feature points in the third step, a box filter is used.
Optionally, the fourth step includes stereo pair selection, depth map calculation, depth map optimization, and depth map fusion.
Optionally, the fifth step includes: defining an octree, setting a function space, creating a vector field, solving a Poisson equation and extracting an isosurface.
Optionally, the sixth step includes ① obtaining texture data to reconstruct a surface triangular surface grid of the target through the image, ② reconstructing visibility analysis of a model triangular surface, ③ clustering the triangular surfaces to generate texture patches, and ④ automatically sequencing the texture patches to generate texture images.
The invention also provides a memory or a processor for executing the method; a program for executing the method is stored.
Invention and technical effects
1. The method is firstly proposed to improve the synthesis speed and the synthesis precision by increasing the mode that the background plate rotates along with the camera and matching with the optimized algorithm.
2. The method improves the synthesis speed and the synthesis precision by the mode of optimizing the position of the camera for acquiring the picture and the optimized algorithm. And when the position is optimized, the angle and the target size do not need to be measured, and the applicability is stronger.
3. And the algorithm efficiency is improved through the optimized image preprocessing step.
4. An algorithm framework suitable for 3D synthesis is provided, and synthesis efficiency and effect are considered.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method for generating a three-dimensional model according to an embodiment of the present invention;
fig. 2 is a front view of a 3D information acquisition device according to an embodiment of the present invention;
fig. 3 is a perspective view of a 3D information acquisition device according to an embodiment of the present invention;
fig. 4 is another perspective view of a 3D information collecting apparatus according to an embodiment of the present invention;
the correspondence of reference numerals to the respective components is as follows:
the device comprises an image acquisition device 1, a rotating device 2, a background plate 3, a first mounting column 4, a rotating beam 5, a horizontal support 6 and a second mounting column 7.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below 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 embodied 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.
Three-dimensional model generation method
Referring to fig. 1, the first step (S1): and acquiring multiple groups of images of the target object by using the 3D information acquisition equipment. The rotating device is used to enable the acquisition area of the image acquisition device and the target object to generate relative motion, so that the image acquisition device can acquire multiple groups of images of the target object in different directions. The image acquisition device can rotate and the target object keeps still through the rotating arm and the rotating disk, the target object can also rotate and the image acquisition device is still, of course, both the images can also move, and the image acquisition device only needs to be capable of acquiring multiple groups of images of the target object in different directions. In addition to the above, a plurality of cameras may be provided around the object so as to capture images of the object from different angles, respectively. Specific example apparatus and processes are described in detail below.
Second step (S2): and performing image enhancement processing on all input photos. The contrast of the original picture is enhanced and simultaneously the noise suppressed using the following filters.
In the formula: g (x, y) is the gray value of the original image at (x, y), f (x, y) is the gray value of the original image at the position after being enhanced by the Wallis filter, and mgIs the local gray average value, s, of the original imagegIs the local standard deviation of gray scale of the original image, mfFor the transformed image local gray scale target value, sfC ∈ (0, 1) is the spreading constant of the image variance, and b ∈ (0, 1) is the image brightness coefficient constant.
The filter can greatly enhance image texture modes of different scales in an image, so that the quantity and the precision of feature points can be improved when the point features of the image are extracted, and the reliability and the precision of a matching result are improved in photo feature matching.
The method mainly comprises the steps of constructing ① a Hessian matrix to generate all interest points for feature extraction, aiming at generating stable edge points (harchart points) of an image by using integral images, constructing ② a scale space feature position, comparing each pixel point processed by the Hessian matrix with 26 points in a two-dimensional image space and scale space neighborhood, preliminarily filtering and positioning the points, obtaining a key point with weak energy comparison and a key point with wrong positioning, finally obtaining a maximum characteristic point in a vertical direction of a fan-shaped feature vector 364, and determining a vertical characteristic point vector of a fan-shaped feature vector 364, taking a vertical characteristic point vector of a fan-shaped feature vector 364 as a vertical characteristic vector of a fan-shaped feature vector 364, taking a vertical characteristic vector of a fan-shaped feature vector 364 as a vertical characteristic vector of a vertical characteristic vector 364, taking a vertical characteristic vector of a fan-shaped feature vector 364 as a vertical characteristic vector of a vertical characteristic vector 364, and a vertical characteristic vector 364 as a vertical characteristic vector of a fan-shaped feature vector 364.
Fourth step (S4): inputting matched feature point coordinates, resolving the sparse three-dimensional point cloud of the target object and the position and posture data of the photographing camera by using a light beam method adjustment, namely obtaining model coordinate values of the sparse three-dimensional point cloud of the target object model and the position; and performing multi-view photo dense matching by taking the sparse feature points as initial values to obtain dense point cloud data. The process mainly comprises four steps: stereo pair selection, depth map calculation, depth map optimization and depth map fusion. For each image in the input data set, we select a reference image to form a stereo pair for use in computing the depth map. Therefore, we can get rough depth maps of all images, which may contain noise and errors, and we use its neighborhood depth map to perform consistency check to optimize the depth map of each image. And finally, carrying out depth map fusion to obtain the three-dimensional point cloud of the whole scene.
Fifth step (S5): and reconstructing the curved surface of the target object by using the dense point cloud. The method comprises the steps of defining an octree, setting a function space, creating a vector field, solving a Poisson equation and extracting an isosurface. And obtaining an integral relation between the sampling point and the indicating function according to the gradient relation, obtaining a vector field of the point cloud according to the integral relation, and calculating the approximation of the gradient field of the indicating function to form a Poisson equation. And (3) solving an approximate solution by using matrix iteration according to a Poisson equation, extracting an isosurface by adopting a moving cube algorithm, and reconstructing a model of the measured point cloud.
The method comprises the following steps of (S6) fully automatically mapping textures of a target object model, performing texture mapping after the surface model is built, wherein the main process comprises the steps of obtaining ① texture data and reconstructing a surface triangular surface grid of the target through an image, conducting ② visibility analysis on a reconstructed model triangular surface, calculating a visible image set and an optimal reference image of each triangular surface by using calibration information of the image, clustering ③ triangular surfaces to generate texture patches, clustering the triangular surfaces into a plurality of reference image texture patches according to the visible image set, the optimal reference image and neighborhood topological relations of the triangular surfaces, automatically sequencing ④ texture patches to generate texture images, sequencing the generated texture patches according to the size relations of the texture patches, generating a texture image with the minimum surrounding area, and obtaining texture mapping coordinates of each triangular surface.
3D information acquisition equipment structure
In order to improve the efficiency of the algorithm, please refer to fig. 2-4, the invention provides a 3D information acquisition device matched with the algorithm, which comprises an image acquisition device 1, a rotating beam 5, a rotating device 2 and a background plate 3.
The two ends of the rotating beam 5 are respectively connected with the image acquisition device 1 and the background plate 3 which are arranged oppositely, and the rotating device 2 drives the image acquisition device 1 and the background plate 3 to rotate synchronously, so that the image acquisition device 1 can acquire images of different colors and images of different colors. The rotating beam 5 is connected with the fixed beam through the rotating device 2, the rotating device 2 drives the rotating beam 5 to rotate, so that the background plate 3 and the image acquisition device 1 at two ends of the beam are driven to rotate, however, no matter how the background plate rotates, the image acquisition device 1 and the background plate 3 are arranged oppositely, and particularly, the optical axis of the image acquisition device 1 penetrates through the center of the background plate 3.
The image capturing device 1 is used for capturing an image of an object, and may be a fixed focus camera or a zoom camera. In particular, the camera may be a visible light camera or an infrared camera. Of course, it should be understood that any device with image capture capability may be used and is not intended to limit the present invention.
The background plate 3 is entirely of a solid color, or mostly (body) of a solid color. In particular, the color plate can be a white plate or a black plate, and the specific color can be selected according to the color of the object body. The background plate 3 is generally a flat plate, and preferably a curved plate, such as a concave plate, a convex plate, a spherical plate, and even in some application scenarios, the background plate 3 with a wavy surface; the plate can also be made into various shapes, for example, three sections of planes can be spliced to form a concave shape as a whole, or a plane and a curved surface can be spliced. In addition to the surface shape of the background plate being variable, the edge shape may be selected as desired. Typically rectilinear, to form a rectangular plate. But in some applications the edges may be curved. Preferably, the background plate 3 is a curved plate, so that the projection size of the background plate 3 can be minimized in the case of obtaining the maximum background range. This makes the background plate 3 require a smaller space when rotating, which is advantageous for reducing the volume of the apparatus, and reducing the weight of the apparatus, avoiding the rotation inertia, and thus being more advantageous for controlling the rotation.
The light source can be L ED light source, also can be intelligent light source, namely the light source parameter of automatic adjustment according to the situation of object and ambient light, under normal circumstances, the light source is located at the peripheral decentralized distribution of lens of the image acquisition device 1, for example, the light source is the annular L ED lamp at the periphery of the lens, because in some application, the object to be gathered is the human body, therefore need control the light source intensity, avoid causing the human body to be uncomfortable, especially can set up the soft light device in the light path of the light source, for example for soft light shell, or adopt L ED area light source directly, not only the light is softer, and it is more even to shine light-emitting.
Between the image capturing device 1 and the background plate 3 is typically the object to be captured. When the object is a human body, a seat may be provided in the center of the base of the apparatus. And because the height of different people is different, the seat can be set up to connect liftable structure. The lifting mechanism is driven by a driving motor and is controlled to lift by a remote controller. Of course, the lifting mechanism can also be controlled by the control terminal in a unified way. Namely, the control panel of the driving motor communicates with the control terminal in a wired or wireless mode to receive the command of the control terminal. The control terminal can be a computer, a cloud platform, a mobile phone, a tablet, a special control device and the like.
However, when the target is an object, a stage may be provided at the center of the base of the apparatus. Similarly, the object stage can be driven by the lifting structure to adjust the height so as to conveniently acquire the information of the target object. The specific control method and connection relationship are the same as those described above, and are not described in detail. However, in particular, unlike a human being, the object does not cause discomfort when rotating, and therefore the stage can be rotated by the rotating device 2, and the rotating beam 5 is not required to rotate to drive the image capturing device 1 and the background plate 3 to rotate during capturing. Of course, the stage and the rotating beam 5 may be rotated simultaneously.
To facilitate the actual size measurement of the object, 4 markers may be placed on the seat or stage, and the coordinates of these markers are known. The absolute size of the 3D synthetic model is obtained by collecting the mark points and combining the coordinates thereof. The marker points may be located on a head rest on the seat.
The device further comprises a processor, also called processing unit, for synthesizing a 3D model of the object according to the plurality of images acquired by the image acquisition means and according to a 3D synthesis algorithm, to obtain 3D information of the object.
Background plate size optimization
Regardless of the surface shape and edge shape of the background plate 3, the projection is performed in a direction perpendicular to the surface to be photographed, and the projection shape has a length W in the horizontal direction1Length W in the vertical direction of the projected shape2Is determined by the following conditions:
wherein d is1For imaging elementLength of the member in the horizontal direction, d2Is the length of the imaging element in the vertical direction, T is the vertical distance from the sensing element of the image acquisition device to the background plate in the direction of the optical axis, f is the focal length of the image acquisition device, A1、A2Are empirical coefficients.
After a large number of experiments, preferably, A1>1.04，A2>1.04; more preferably 2>A1>1.1，2>A2>1.1。
In some application scenarios, the edge of the background plate 3 is non-linear, which results in the projected image edge being non-linear after projection. At this time, W is measured at different positions1、W2All are different, so that W is actually calculated1、W2It is not easy to determine. Therefore, it is possible to take 3 to 5 points on the opposite sides of the background plate at the edges, respectively, measure the linear distances between the opposite points, and take the average of the measurements as W in the above condition1、W2。
If the background plate 3 is too large, making the cantilever too long, the volume of the device will increase, at the same time placing an extra burden on the rotation, making the device more vulnerable. However, if the background plate 3 is too small, the background is not simple, and the calculation load is increased.
The following table shows experimental control results:
the experimental conditions are as follows:
acquiring an object: head of plaster portrait
A camera: MER-2000-19U3M/C
Lens: OPT-C1616-10M
| Empirical coefficient | Time of synthesis | Synthetic accuracy |
| A1＝1.2，A2＝1.2 | 3.3 minutes | Height of |
| A1＝1.4，A2＝1.4 | 3.4 minutes | Height of |
| A1＝0.9，A2＝0.9 | 4.5 minutes | Middle and high |
| Is free of | 7.8 minutes | In |
3D information acquisition method flow
The object is placed between the image capture device 1 and the background plate 3. Preferably on the extension of the rotation axis of the rotating device 2, i.e. at the center of the circle around which the image capturing device 1 rotates. Therefore, the distance between the image acquisition device 1 and the target object is basically unchanged in the rotation process, so that the situation that the image acquisition is not clear due to the drastic change of the object distance or the requirement on the depth of field of the camera is too high (the cost is increased) is prevented.
When the subject is a head of a human body, a seat may be placed between the image pickup device 1 and the background plate 3, and when the human is seated, the head is located right near the rotation axis and between the image pickup device 1 and the background plate 3. Since each person is of a different height, the height of the area to be collected (e.g. the head of a person) is different. The position of the human head in the visual field of the image acquisition device 1 can be adjusted by adjusting the height of the seat. When the collection of object is carried out, can put the thing platform with seat replacement.
In addition to adjusting the height of the seat, the center of the target object can be ensured to be located at the center of the field of view of the image capturing device 1 by adjusting the height of the image capturing device 1 and the height of the background plate 3 in the vertical direction. For example, the background plate 3 may be moved up and down along the first mounting post 4 and the horizontal bracket 6 carrying the image capturing mechanism 1 may be moved up and down along the second mounting post 7. Typically, the movement of the background plate 3 and the image capturing device 1 is synchronized, ensuring that the optical axis of the image capturing device 1 passes through the center position of the background plate 3.
The size of the target object is greatly different in each acquisition. If the image acquisition device 1 acquires images at the same position, the ratio of the target object in the images can be changed greatly. For example, when the size of the object a is proper in the image, if the object B is changed to be a smaller object, the proportion of the object B in the image will be very small, which greatly affects the subsequent 3D synthesis speed and accuracy. Therefore, the image acquisition device can be driven to move back and forth on the horizontal support 6, and the proportion of the target object in the picture acquired by the image acquisition device 1 is ensured to be proper.
The object is ensured to be basically fixed, the rotating device 2 drives the image acquisition device 1 and the background plate 3 to rotate around the object by rotating the rotating beam 5, and the two are ensured to be opposite in the rotating process. When the collection is carried out in the rotating process, the collection can be continuously rotated and collected at fixed angles; or stopping rotating at the position with a fixed interval angle for collection, continuing rotating after collection, and continuing stopping rotating at the next position for collection.
3D acquisition camera position optimization
According to a number of experiments, the separation distance of the acquisitions preferably satisfies the following empirical formula:
when 3D acquisition is performed, the two adjacent acquisition positions of the image acquisition device 1 satisfy the following conditions:
wherein L is the linear distance of the optical center of the image acquisition device when two adjacent acquisition positions are located, f is the focal length of the image acquisition device, d is the rectangular length or width of the photosensitive element (CCD) of the image acquisition device, T is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis, and T is an adjustment coefficient of < 0.603.
When the two positions are along the length direction of the photosensitive element of the image acquisition device, d is a rectangle; when the two positions are along the width direction of the photosensitive element of the image acquisition device, d is in a rectangular width.
The distance of the photosensitive element to the surface of the object along the optical axis when the image pickup device is in any one of the two positions is taken as T in another case L is A in addition to this methodn、An+1Linear distance between optical centers of two image capturing devices, and An、An+1Two image acquisition devices adjacent to each othern-1、An+2Two image acquisition devices and An、An+1The distances from the respective photosensitive elements of the two image acquisition devices to the surface of the target object along the optical axis are respectively Tn-1、Tn、Tn+1、Tn+2，T＝(Tn-1+Tn+Tn+1+Tn+2)/4. Of course, the average value may be calculated by using more positions than the adjacent 4 positions.
L should be a straight line distance between the optical centers of the two image capturing devices, but since the position of the optical center of the image capturing device is not easily determined in some cases, the center of the photosensitive element of the image capturing device, the geometric center of the image capturing device, the center of the axis connecting the image capturing device and the pan/tilt head (or platform, support), and the center of the proximal or distal surface of the lens can be used instead in some cases, and the error caused by the replacement can be found to be within an acceptable range through experiments.
In general, parameters such as object size and angle of view are used as means for estimating the position of a camera in the prior art, and the positional relationship between two cameras is also expressed in terms of angle. Because the angle is not well measured in the actual use process, it is inconvenient in the actual use. Also, the size of the object may vary with the variation of the measurement object. For example, when the head of a child is collected after 3D information on the head of an adult is collected, the head size needs to be measured again and calculated again. The inconvenient measurement and the repeated measurement bring errors in measurement, thereby causing errors in camera position estimation. According to the scheme, the experience conditions required to be met by the position of the camera are given according to a large amount of experimental data, so that the problem that the measurement is difficult to accurately measure the angle is solved, and the size of an object does not need to be directly measured. In the empirical condition, d and f are both fixed parameters of the camera, and corresponding parameters can be given by a manufacturer when the camera and the lens are purchased without measurement. And T is only a straight line distance, and can be conveniently measured by using a traditional measuring method, such as a ruler and a laser range finder. Therefore, the empirical formula of the invention enables the preparation process to be convenient and fast, and simultaneously improves the arrangement accuracy of the camera position, so that the camera can be arranged in an optimized position, thereby simultaneously considering the 3D synthesis precision and speed, and the specific experimental data is shown in the following.
Experiments were conducted using the apparatus of the present invention, and the following experimental results were obtained.
The camera lens is replaced, and the experiment is carried out again, so that the following experiment results are obtained.
The camera lens is replaced, and the experiment is carried out again, so that the following experiment results are obtained.
From the above experimental results and a lot of experimental experiences, it can be derived that the value should satisfy <0.603, and at this time, a part of the 3D model can be synthesized, although a part cannot be automatically synthesized, it is acceptable in the case of low requirements, and the part which cannot be synthesized can be compensated manually or by replacing the algorithm. Particularly, when the value satisfies <0.410, the balance between the synthesis effect and the synthesis time can be optimally taken into consideration; to obtain better synthesis results, <0.356 can be chosen, where the synthesis time will increase, but the synthesis quality is better. Of course, <0.311 may be selected to further improve the effect of the synthesis. And 0.681, the synthesis is not possible. It should be noted that the above ranges are only preferred embodiments and should not be construed as limiting the scope of protection.
Moreover, as can be seen from the above experiment, for the determination of the photographing position of the camera, only the camera parameters (focal length f, CCD size) and the distance T between the camera CCD and the object surface need to be obtained according to the above formula, which makes it easy to design and debug the device. Since the camera parameters (focal length f, CCD size) are determined at the time of purchase of the camera and are indicated in the product description, they are readily available. Therefore, the camera position can be easily calculated according to the formula without carrying out complicated view angle measurement and object size measurement. Particularly, in some occasions, the lens of the camera needs to be replaced, and then the position of the camera can be obtained by directly replacing the conventional parameter f of the lens and calculating; similarly, when different objects are collected, the measurement of the size of the object is complicated due to the different sizes of the objects. By using the method of the invention, the position of the camera can be determined more conveniently without measuring the size of the object. And the camera position determined by the invention can give consideration to both the synthesis time and the synthesis effect. Therefore, the above-described empirical condition is one of the points of the present invention.
The above data are obtained by experiments for verifying the conditions of the formula, and do not limit the invention. Without these data, the objectivity of the formula is not affected. Those skilled in the art can adjust the equipment parameters and the step details as required to perform experiments, and obtain other data which also meet the formula conditions.
The rotation movement of the invention is that the front position collection plane and the back position collection plane are crossed but not parallel in the collection process, or the optical axis of the front position image collection device and the optical axis of the back position image collection device are crossed but not parallel. That is, the capture area of the image capture device moves around or partially around the target, both of which can be considered as relative rotation. Although the embodiment of the present invention exemplifies more orbital rotation, it should be understood that the limitation of the present invention can be used as long as the non-parallel motion between the acquisition region of the image acquisition device and the target object is rotation. The scope of the invention is not limited to the embodiment with track rotation.
The adjacent acquisition positions refer to two adjacent positions on a movement track where acquisition actions occur when the image acquisition device moves relative to a target object. This is generally easily understood for the image acquisition device movements. However, when the target object moves to cause relative movement between the two, the movement of the target object should be converted into the movement of the target object, which is still, and the image capturing device moves according to the relativity of the movement. And then measuring two adjacent positions of the image acquisition device in the converted movement track.
Utilization of three-dimensional models
By using the method, a three-dimensional model of the target object can be synthesized, so that the real physical world object is completely digitalized. The digitalized information can be used for identifying and comparing objects, product design, 3D display, medical assistance and other purposes.
For example, after the three-dimensional information of the face is collected, the three-dimensional information can be used as a basis for identification and comparison to perform 3D identification on the face.
For example, a more conformable garment may be designed for a user using a three-dimensional model of the human body.
For example, after a three-dimensional model of a workpiece is generated, 3D printing can be directly performed.
For example, after a three-dimensional model of the interior of the body is generated, the body information can be digitized for use in simulating surgical procedures for medical teaching.
The target object, and the object all represent objects for which three-dimensional information is to be acquired. The object may be a solid object or a plurality of object components. For example, the head, hands, etc. The three-dimensional information of the target object comprises a three-dimensional image, a three-dimensional point cloud, a three-dimensional grid, a local three-dimensional feature, a three-dimensional size and all parameters with the three-dimensional feature of the target object. Three-dimensional in the present invention means having XYZ three-direction information, particularly depth information, and is essentially different from only two-dimensional plane information. It is also fundamentally different from some definitions, which are called three-dimensional, panoramic, holographic, three-dimensional, but actually comprise only two-dimensional information, in particular not depth information.
The capture area in the present invention refers to a range in which an image capture device (e.g., a camera) can capture an image. The image acquisition device can be a CCD, a CMOS, a camera, a video camera, an industrial camera, a monitor, a camera, a mobile phone, a tablet, a notebook, a mobile terminal, a wearable device, intelligent glasses, an intelligent watch, an intelligent bracelet and all devices with image acquisition functions.
In the description provided herein, numerous specific details are set forth. It is understood, however, 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 in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in an apparatus in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (18)
1. A three-dimensional model generation method is characterized in that:
the first step is as follows: acquiring a plurality of groups of images of a target object by using 3D information acquisition equipment;
the second step is that: performing image enhancement processing on all input photos;
the third step: extracting characteristic points of all input images, and matching the characteristic points to obtain sparse characteristic points;
the fourth step: inputting matched feature point coordinates, and resolving the sparse human face three-dimensional point cloud and the position and posture data of the image acquisition device to obtain a sparse target object model three-dimensional point cloud and model coordinate values of the position; taking the sparse feature points as initial values, performing multi-view image dense matching, and obtaining dense point cloud data;
the fifth step: reconstructing a curved surface of the target object by using the dense point cloud;
and a sixth step: performing texture mapping on the target object model;
two adjacent acquisition positions of an image acquisition device in the acquisition equipment in the first step meet the following conditions:
wherein L is the straight-line distance of the optical center of the image acquisition device when the two acquisition positions are adjacent, f is the focal length of the image acquisition device, d is the rectangular length or width of the photosensitive element of the image acquisition device, T is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis, and T is an adjustment coefficient of < 0.603.
2. The method of claim 1, wherein: the first step is that the acquisition equipment comprises an image acquisition device, a rotating device and a background plate.
3. The method of claim 1, wherein: the image enhancement processing in the second step comprises:
the contrast of the original picture is enhanced and the noise is suppressed at the same time by adopting the following filter;
in the formula: g (x, y) is the gray value of the original image at (x, y), f (x, y) is the gray value of the original image at the position after being enhanced by the Wallis filter, and mgIs the local gray average value, s, of the original imagegIs the local standard deviation of gray scale of the original image, mfFor the transformed image local gray scale target value, sfC ∈ (0, 1) is the image after transformationThe spreading constant of the variance, b ∈ (0, 1), is the image luminance coefficient constant.
4. The method of claim 1, wherein the third step comprises ① constructing Hessian matrix to generate all interest points for feature extraction, ② constructing scale space feature point positioning, ③ determining feature point main direction, ④ generating 64-dimensional feature point description vector, and ⑤ feature point matching.
5. The method of claim 4, wherein: and in the third step, when the Hessian matrix is used for detecting the characteristic points, a box filter is used.
6. The method of claim 1, wherein: and the fourth step comprises stereopair selection, depth map calculation, depth map optimization and depth map fusion.
7. The method of claim 1, wherein: the fifth step includes: defining an octree, setting a function space, creating a vector field, solving a Poisson equation and extracting an isosurface.
8. The method of claim 1, wherein the sixth step comprises ① obtaining texture data for reconstructing a surface triangular face mesh of the object from the image, ② reconstructing visibility analysis of the triangular faces of the model, ③ clustering the triangular faces to generate texture patches, and ④ automatically ordering the texture patches to generate texture images.
9. A three-dimensional model generation apparatus characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the acquisition equipment is used for acquiring a plurality of groups of images of the target object;
the processor is used for executing the following steps two to six:
step two, performing image enhancement processing on all input photos;
thirdly, extracting characteristic points of all input images, and matching the characteristic points to obtain sparse characteristic points;
inputting matched feature point coordinates, and resolving the sparse human face three-dimensional point cloud and the position and posture data of the image acquisition device to obtain a sparse target object model three-dimensional point cloud and model coordinate values of the position; taking the sparse feature points as initial values, performing multi-view image dense matching, and obtaining dense point cloud data;
fifthly, reconstructing a curved surface of the target object by using the dense point cloud;
step six, performing texture mapping on the target object model;
wherein, two adjacent collection positions of the image collection device in the collection equipment meet the following conditions:
wherein L is the straight-line distance of the optical center of the image acquisition device when the image acquisition device is at two adjacent acquisition positions, f is the focal length of the image acquisition device, d is the rectangular length or width of the photosensitive element of the image acquisition device, T is the distance from the photosensitive element of the image acquisition device to the surface of the target along the optical axis, and T is an adjustment coefficient of < 0.490.
10. The apparatus of claim 9, wherein: the acquisition equipment comprises an image acquisition device, a rotating device and a background plate.
11. The apparatus of claim 9, wherein: the image enhancement processing in the second step comprises:
the contrast of the original picture is enhanced and the noise is suppressed at the same time by adopting the following filter;
in the formula: g (x, y) is the gray value of the original image at (x, y), f (x, y) is the gray value of the original image at the position after being enhanced by the Wallis filter, and mgIs the local gray average value, s, of the original imagegIs the local standard deviation of gray scale of the original image, mfTo becomeTarget value of local gray scale of image after conversion, sfFor the transformed image local gray standard deviation target value, c ∈ (0, 1) is the spreading constant of the image variance, and b ∈ (0, 1) is the image luminance coefficient constant.
12. The apparatus of claim 9, wherein the third step comprises ① constructing Hessian matrix to generate all interest points for feature extraction, ② constructing scale space feature point location, ③ determining feature point principal direction, ④ generating 64-dimensional feature point description vector, ⑤ feature point matching.
13. The apparatus of claim 12, wherein: and in the third step, when the Hessian matrix is used for detecting the characteristic points, a box filter is used.
14. The apparatus of claim 9, wherein: and the fourth step comprises stereopair selection, depth map calculation, depth map optimization and depth map fusion.
15. The apparatus of claim 9, wherein: the fifth step includes: defining an octree, setting a function space, creating a vector field, solving a Poisson equation and extracting an isosurface.
16. The apparatus of claim 9, wherein the sixth step comprises ① obtaining texture data for reconstructing a surface triangular face mesh of the object from the image, ② reconstructing visibility analysis of the triangular faces of the model, ③ clustering the triangular faces to generate texture patches, and ④ automatically ordering the texture patches to generate texture images.
17. A memory, characterized by: storing a program for performing the method of any one of claims 1 to 8.
18. A processor, characterized in that: performing the method of any one of claims 1-8.
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| CN112254677B (en) * | 2020-10-15 | 2022-10-21 | 天目爱视（北京）科技有限公司 | Multi-position combined 3D acquisition system and method based on handheld device |
| CN112257535B (en) * | 2020-10-15 | 2022-04-08 | 天目爱视（北京）科技有限公司 | Three-dimensional matching equipment and method for avoiding object |
| CN112257536B (en) * | 2020-10-15 | 2022-05-20 | 天目爱视（北京）科技有限公司 | Space and object three-dimensional information acquisition and matching equipment and method |
| CN113566793A (en) * | 2021-06-15 | 2021-10-29 | 北京道达天际科技有限公司 | True orthoimage generation method and device based on unmanned aerial vehicle oblique image |
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| JP5480914B2 (en) * | 2009-12-11 | 2014-04-23 | 株式会社トプコン | Point cloud data processing device, point cloud data processing method, and point cloud data processing program |
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