CN113538552A

CN113538552A - 3D information synthesis image matching method based on image sorting

Info

Publication number: CN113538552A
Application number: CN202110828646.9A
Authority: CN
Inventors: 左忠斌; 左达宇
Original assignee: Tianmu Aishi Beijing Technology Co Ltd
Current assignee: Tianmu Aishi Beijing Technology Co Ltd
Priority date: 2020-02-17
Filing date: 2020-02-17
Publication date: 2021-10-22
Anticipated expiration: 2040-02-17
Also published as: CN113538552B; CN111325780A; CN111325780B

Abstract

The embodiment of the invention provides a 3D information synthesis image matching method based on image sorting, which comprises the following steps: step 1: determining a source image to be matched; step 2: screening images adjacent to the source image; and step 3: matching calculation is carried out on the screened images; and 4, step 4: repeating the step 2-3 on the rest source images to be matched, and finally completing the matching of all the images; aiming at the scheme of the surrounding acquisition of the target object with limited volume, the scheme of reducing the calculation difficulty and time of the matching algorithm by screening adjacent pictures is provided for the first time, and the synthesis speed and precision are considered.

Description

3D information synthesis image matching method based on image sorting

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. These 3D models can be regarded as data of real things, and a complement matching the object can be manufactured by using the data. For example, 3D data of a human foot can be collected to create a more suitable shoe. In addition, these data can also be used to verify identity. For example, the iris 3D model of the human body can be synthesized to be used as identity standard data, the iris 3D data is collected again when the iris 3D model is used, and the identity can be recognized by comparing the iris 3D data with the standard data. However, both factory manufacturing and transaction identification have high requirements on the synthesis speed and accuracy of the 3D model, which would otherwise bring about a great deterioration in the customer experience.

In the prior art, the improvement of the synthesis speed is considered to depend on the optimization of the 3D model reconstruction algorithm. Various algorithms have thus been proposed to improve the reconstruction of 3D models, but with modest results. This is because the common algorithms are all general algorithms, and are suitable for a wider range of scenes. Due to the general scenario, the acquisition is relatively random. For example, when modeling a building, the building is photographed by using an unmanned aerial vehicle, and the flight trajectory of the building is not fixed. That is, the acquisition process is not standard. Therefore, the algorithm is designed for the random process at present, and no algorithm optimization of the surrounding acquisition specially aiming at the fixed program exists in the prior art.

It is also believed in the art that the improvement in accuracy is more dependent on the accuracy of the image acquisition. The use of high resolution cameras naturally improves the image acquisition accuracy and to some extent the accuracy of 3D modeling, but ultrahigh resolution images also bring about an extreme decrease in the synthesis speed.

Moreover, the synthesis speed and the synthesis precision are in a pair of contradictions to some extent, and the improvement of the synthesis speed can lead to the reduction of the final 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 following technical problems are urgently needed to be solved: firstly, the optimization bias of the algorithm can be broken through, the optimization of a general algorithm is not carried out, and an optimization method of a rotary acquisition scene is searched; the algorithm can be matched with the method for acquiring the image so as to simultaneously improve the synthesis speed and the synthesis precision. And thirdly, generating a scene of a 3D model aiming at the surrounding type acquisition target object, and specially performing algorithm optimization.

Disclosure of Invention

In view of the above, the present invention has been made to provide a method for synthesizing image matching based on 3D information of image sorting that overcomes or at least partially solves the above-mentioned problems.

The invention provides a method for synthesizing image matching based on 3D information of image sorting,

step 1: determining a source image to be matched;

step 2: screening images adjacent to the source image;

and step 3: matching calculation is carried out on the screened images;

and 4, step 4: repeating the step 2-3 on the rest source images to be matched, and finally completing the matching of all the images;

in step 2, according to the rotation speed s of the image acquisition device and the exposure time interval T, the position Pi (Xi, Yi) of any photographing time is calculated, according to the position Pi (Xi, Yi) of the image acquisition device at any photographing time, the distance Di between the current photographing position Pt (Xt, Yt) and the photographing positions at all times is calculated, Di is sorted, the smallest M images are selected as the photographing positions adjacent to Pt, and the images photographed at the corresponding positions are used as the images to be matched.

Optionally, in step 2, the method for calculating the position Pi (Xi, Yi) at any photographing time includes:

the arc length formula is L ═ Nx pi × r/180, where N is the central angle number, r is the radius, and L is the arc length; i.e., N ═ L × 180/(pi × r);

when the camera takes a picture at any exposure time for one week, the arc length of the camera sliding is

Namely the angle between the photographing position of the camera and the X axis

Obtaining the position Xi ═ r × cos (n) of the exposure time Pi at any camera position for one round of photographing, and Yi ═ r × sin (n);

optionally, the distance between adjacent acquisition positions of the two images is:

wherein L is the linear distance of the optical center of the image acquisition device 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; δ is the adjustment coefficient.

Optionally, δ < 0.603.

Alternatively, δ < 0.498.

Alternatively, δ < 0.356.

Optionally, δ < 0.311.

Optionally, the method further includes:

carrying out image enhancement processing on the screened image;

extracting characteristic points of the images obtained by screening, 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;

and performing multi-view image dense matching by taking the sparse feature points as initial values to obtain dense point cloud data.

The invention also provides a method for generating a solid object by using three-dimensional model data, which comprises any one of the matching methods.

The third aspect of the invention also provides a three-dimensional model construction method, which comprises any one of the matching methods.

The fourth aspect of the invention also provides a three-dimensional data comparison method, which comprises any one of the matching methods.

Invention and technical effects

1. Aiming at the scheme of the surrounding acquisition of the target object with limited volume, the scheme of reducing the calculation difficulty and time of the matching algorithm by screening adjacent pictures is provided for the first time, and the synthesis speed and precision are considered.

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. By the method for sorting the photo collecting distances, the photos which are most suitable for matching can be found under any condition, and the most suitable photos cannot be omitted, so that the algorithm speed and the algorithm precision are improved.

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 3D synthesis method provided by an embodiment of the invention;

FIG. 2 is a flowchart of an image screening method according to an embodiment of the present invention;

fig. 3 is a schematic diagram of an implementation manner of a rotation structure of the acquisition device according to an embodiment of the present invention;

fig. 4 is a schematic diagram of another implementation manner of the acquisition device in a rotating structure according to the embodiment of the present invention;

fig. 5 is a schematic diagram of an implementation manner in which the acquisition device provided by the embodiment of the present invention is a translation structure;

fig. 6 is a schematic diagram of an implementation manner of the irregular movement of the acquisition device according to the embodiment of the present invention;

fig. 7 is a schematic diagram of an implementation manner of the multi-camera structure of the acquisition device provided by the embodiment of the present invention;

the correspondence of reference numerals to the respective components is as follows:

the device comprises an object stage 1, a rotating device 2, a rotating arm 3 and an image acquisition device 4.

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.

3D Synthesis Process

The image acquisition device acquires a group of images of the target object through relative movement with the target object; the acquisition device is described in detail in the acquisition device embodiments described below.

The processing unit obtains the 3D information of the target object according to a plurality of images in the group of images. The specific algorithm is as follows. Of course, the processing unit may be directly disposed in the housing where the image capturing device is located, or may be connected to the image capturing device through a data line or in a wireless manner. For example, an independent computer, a server, a cluster server, or the like may be used as a processing unit, and image data acquired by the image acquisition device may be transmitted thereto to perform 3D synthesis. Meanwhile, the data of the image acquisition device can be transmitted to the cloud platform, and 3D synthesis is performed by utilizing the strong computing power of the cloud platform.

When the acquired picture is used for 3D synthesis, the existing algorithm can be adopted, and the optimized algorithm proposed by the present invention can also be adopted, as shown in fig. 1, the method mainly comprises the following steps:

step 10: 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 m_gIs the local gray average value, s, of the original image_gIs the local standard deviation of gray scale of the original image, m_fFor the transformed image local gray scale target value, s_fThe target value of the standard deviation of the local gray scale of the image after transformation. c belongs to (0, 1) as the expansion constant of the image variance, and b belongs to (0, 1) as 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.

Step 20: and extracting feature points of all input photos, and matching the feature points to obtain sparse feature points. And extracting and matching feature points of the photos by adopting a SURF operator. The SURF feature matching method mainly comprises three processes of feature point detection, feature point description and feature point matching. The method uses a Hessian matrix to detect characteristic points, a Box filter (Box Filters) is used for replacing second-order Gaussian filtering, an integral image is used for accelerating convolution to improve the calculation speed, and the dimension of a local image characteristic descriptor is reduced to accelerate the matching speed. The method mainly comprises the steps of firstly, constructing a Hessian matrix, generating all interest points for feature extraction, and constructing the Hessian matrix for generating stable edge points (catastrophe points) of an image; secondly, establishing scale space characteristic point positioning, comparing each pixel point processed by the Hessian matrix with 26 points in a two-dimensional image space and a scale space neighborhood, preliminarily positioning a key point, filtering the key point with weak energy and the key point with wrong positioning, and screening out a final stable characteristic point; and thirdly, determining the main direction of the characteristic points by adopting the harr wavelet characteristics in the circular neighborhood of the statistical characteristic points. In the circular neighborhood of the feature point, counting the sum of horizontal and vertical harr wavelet features of all points in a sector of 60 degrees, then rotating the sector at intervals of 0.2 radian, counting the harr wavelet feature value in the region again, and finally taking the direction of the sector with the maximum value as the main direction of the feature point; and fourthly, generating a 64-dimensional feature point description vector, and taking a 4-by-4 rectangular region block around the feature point, wherein the direction of the obtained rectangular region is along the main direction of the feature point. Each subregion counts haar wavelet features of 25 pixels in both the horizontal and vertical directions, where both the horizontal and vertical directions are relative to the principal direction. The haar wavelet features are in 4 directions of the sum of the horizontal direction value, the vertical direction value, the horizontal direction absolute value and the vertical direction absolute value, and the 4 values are used as feature vectors of each sub-block region, so that a total 4 x 4-64-dimensional vector is used as a descriptor of the Surf feature; and fifthly, matching the characteristic points, wherein the matching degree is determined by calculating the Euclidean distance between the two characteristic points, and the shorter the Euclidean distance is, the better the matching degree of the two characteristic points is.

Step 30: inputting matched feature point coordinates, resolving sparse human face three-dimensional point cloud and position and posture data of a photographing camera by using a light beam method adjustment, namely obtaining model coordinate values of the sparse human face model three-dimensional point cloud 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.

Step 40: and reconstructing a human face curved surface 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.

Step 50: and (4) fully-automatic texture mapping of the human face model. And after the surface model is constructed, texture mapping is carried out. The main process comprises the following steps: texture data is obtained to reconstruct a surface triangular surface grid of a target through an image; and secondly, reconstructing the visibility analysis of the triangular surface of the model. Calculating a visible image set and an optimal reference image of each triangular surface by using the calibration information of the image; and thirdly, clustering the triangular surface to generate a texture patch. Clustering the triangular surfaces into a plurality of reference image texture patches according to the visible image set of the triangular surfaces, the optimal reference image and the neighborhood topological relation of the triangular surfaces; and fourthly, automatically sequencing the texture patches to generate texture images. And sequencing the generated texture patches according to the size relationship of the texture patches to generate a texture image with the minimum surrounding area, and obtaining the texture mapping coordinate of each triangular surface.

It should be noted that the above algorithm is an optimization algorithm of the present invention, the algorithm is matched with the image acquisition condition, and the use of the algorithm takes account of the time and quality of the synthesis, which is one of the inventions of the present invention. Of course, it can be implemented using conventional 3D synthesis algorithms in the prior art, except that the synthesis effect and speed are somewhat affected.

Image screening method

In the synthesis using the above method, the most important step is matching of feature points. Usually, matching calculation is performed on one image and the other acquired images, so that the image matched with the image can be determined. The algorithm does not need to consider the source of the image, and is wider in applicability. But obviously such a calculation is very computationally intensive. The present invention has found through a number of experiments that in practice the best match for each image is the image of its periphery, i.e. the picture with which it overlaps. Therefore, the method screens out the most adjacent images of each image for calculation of the matching algorithm, so that matching can be completed in the calculation process of the first images at a high probability, calculation of the matching algorithm for all the images is not needed, and the matching efficiency can be greatly improved. As shown in fig. 2, the specific screening method is as follows:

1. according to the rotation speed s of the camera and the exposure time interval T, the position Pi (Xi, Yi) of any photographing time can be calculated, and the specific process is as follows:

1-1 is based on the arc length L-Nxπ × r/180, where N is the central angle number, r is the radius, and L is the arc length. N ═ L × 180/(pi × r);

1-2 random exposure time of camera photographing for one week and arc length of camera sliding

1-3 obtaining the angle between the photographing position of the camera and the X axis

1-4, obtaining the position Xi ═ r × cos (n) of the exposure time Pi at any camera position for one photographing cycle, and Yi ═ r × sin (n);

2. and calculating the distances Di between the current photographing position Pt (Xt, Yt) and the photographing positions at all the moments according to the camera positions Pi (Xi, Yi) at any photographing moment, sequencing Di, and selecting the minimum M photographing positions adjacent to Pt. Typically M may be 4, 5 … 10.

3. And (4) calculating all the positions in the step (2) to obtain the adjacent position of each photographing position. I.e. all neighbouring photos of each photo are also obtained.

4. And carrying out matching calculation on the picture and all adjacent pictures by utilizing the step of the 3D synthetic method flow, and finally carrying out modeling. In this case, the input photograph in the "3D synthesis method flow" is not all photographs, but the photograph screened out as described above.

In addition to the above methods, other methods may be used to screen images, all for the purpose of obtaining images that are adjacent before and after a certain image. For example:

1. the camera starts to rotate from an initial position, and starts to shoot a first image after the rotating speed of the stepping motor is stable, wherein the number of the first image is 1;

2. the second photographing position is determined according to the condition of the adjacent acquisition position in the "image acquisition device position optimization" (as follows), and the second image, numbered 2,

δ<0.603

wherein L is the linear distance between the optical centers of the two adjacent image acquisition positions; 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; δ is the adjustment coefficient.

3. And the shooting position adjacent to the second shooting position by the distance L is a third shooting position, the number of the shot third image is 3, and by analogy, the nth image is shot according to the conditions, and the number is n.

4. When the 3D synthesis method flow is used for modeling, images with numbers n-2, n-1, n +1 and n +2 are respectively selected for matching calculation. Of course, more pictures, such as n-m … n-1, n +1 … n + m, may be selected, i.e., 2m images adjacent to the number n are selected as matching objects.

By the method, when the input photos are 100, the synthesis speed is improved by 69.7%, and the synthesis precision and the integrity are improved by 16.1%; when the number of input photos is 1000, the synthesis speed is improved by 90.1%, and the synthesis precision and the integrity are improved by 17.3%.

Collection equipment

In order to realize the acquisition of 3D information, the invention provides image acquisition equipment for acquiring 3D information, which comprises an image acquisition device and a rotating device. The image acquisition device is used for acquiring a group of images of the target object through the relative movement of an acquisition area of the image acquisition device and the target object; and the acquisition area moving device is used for driving the acquisition area of the image acquisition device to generate relative motion with the target object. The collection area is the effective field range of the image collection device. The structure of the specific acquisition equipment has different forms as follows:

acquisition equipment with acquisition area moving device of rotating structure

Referring to fig. 3, the object is fixed on the object stage 1, the rotating device 2 includes a rotation driving device and a rotating arm 3, wherein the rotation driving device can be located above the object to drive the rotating arm 3 to rotate, the rotating arm 3 is connected with a vertical column extending downward, and the vertical column is provided with an image collecting device 4. The image acquisition device 4 is rotated around the object by the driving of the rotating device 2.

In another case, referring to fig. 4, the apparatus includes a circular stage 1 for carrying a target object; the rotating device 2 comprises a rotating driving device and a rotating arm 3, wherein the rotating arm 3 is bent, and the horizontal lower section part is rotationally fixed on the base, so that the vertical upper section part rotates around the objective table 1; the image acquisition device 4 is used for acquiring images of the target object and is arranged at the upper section of the rotating arm, and the special image acquisition device 4 can also rotate vertically along the rotating arm in a pitching manner so as to adjust the acquisition angle.

In fact, the manner of rotating the image capturing device around the object is not limited to the above, and various structures such as the image capturing device being disposed on an annular track around the object, on a turntable, on a rotating cantilever, etc. may be implemented. Therefore, the image acquisition device only needs to rotate around the target object. Of course, the rotation is not necessarily a complete circular motion, and can be only rotated by a certain angle according to the acquisition requirement. The rotation is not necessarily circular motion, and the motion track of the image acquisition device can be other curved tracks, but the camera is ensured to shoot objects from different angles.

In addition to the above, in some cases, the camera may be fixed, and the stage carrying the target may be rotated, so that the direction of the target facing the image capturing device changes from moment to moment, thereby enabling the image capturing device to capture images of the target from different angles. However, in this case, the calculation may still be performed according to the condition of converting the motion into the motion of the image capturing device, so that the motion conforms to the corresponding empirical formula (which will be described in detail below). For example, in a scenario where the stage rotates, it may be assumed that the stage is stationary and the image capture device rotates. The distance of the shooting position when the image acquisition device rotates is set by using an empirical formula, so that the rotating speed of the image acquisition device is deduced, the rotating speed of the object stage is reversely deduced, the rotating speed is conveniently controlled, and 3D acquisition is realized.

In addition, in order to enable the image acquisition device to acquire images of the target object in different directions, the image acquisition device and the target object can be kept still, and the image acquisition device and the target object can be rotated by rotating the optical axis of the image acquisition device. For example: the collecting area moving device is an optical scanning device, so that the collecting area of the image collecting device and the target object generate relative motion under the condition that the image collecting device does not move or rotate. The acquisition area moving device also comprises a light deflection unit which is driven by machinery to rotate, or is driven by electricity to cause light path deflection, or is distributed in space in multiple groups, so that images of the target object can be acquired from different angles. The light deflection unit may typically be a mirror, which is rotated to collect images of the target object in different directions. Or a reflector surrounding the target object is directly arranged in space, and the light of the reflector enters the image acquisition device in turn. Similarly to the foregoing, the rotation of the optical axis in this case can be regarded as the rotation of the virtual position of the image pickup device, and by this method of conversion, it is assumed that the image pickup device is rotated, so that the calculation is performed using the following empirical formula.

The image acquisition device is used for acquiring an image of a target object and can 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 is understood that any device with image capturing function can be used, and does not limit the present invention, and for example, the 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, a smart glasses, a smart watch, a smart bracelet, and all devices with image capturing function.

A background plate may also be added to the device when the arrangement is rotated. The background plate is positioned opposite to the image acquisition device, synchronously rotates when the image acquisition device rotates, and keeps still when the image acquisition device is still. And the image of the target object collected by the image collecting device is all with the background plate as the background. Of course, it is also possible to set a completely fixed background plate for the object so that the background plate can be used as the capturing background regardless of the movement of the image capturing apparatus. The background plate is all solid or mostly (body) solid. 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 is usually 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 may have 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.

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.

In addition to the above-mentioned rotation manner, in some situations, it is difficult to have a large space for accommodating the rotation of the rotating device. In this case, the rotation space of the rotating device is limited. For example, the rotation device may include a rotation driving device and a rotation arm, wherein the rotation track of the rotation arm has a smaller distance from the rotation center or the center line of the rotation arm coincides (or approximately coincides) with the rotation center line. The rotation driving device may include a motor directly connected to the linear type rotor arm through a gear, and at this time, a physical center line of the rotor arm coincides with a rotation center line of the rotor arm. In another case, the swivel arm is L-shaped, comprising a crossbar and a vertical arm. The cross arm of the rotating arm is connected with the rotating driving device, and the image acquisition device is installed on the vertical arm. The rotation driving device comprises a motor, the motor drives the cross arm to rotate, the vertical arm fixedly connected with the cross arm correspondingly rotates, and at the moment, the rotation center line is not overlapped with the physical center line of the vertical arm. Generally, to save space for rotation, the distance of such misalignment can be reduced appropriately, or the cross arm size can be reduced appropriately. Of course, when the L-shaped rotating arm is used, the vertical arm of the L-shaped rotating arm can be placed in a target object, and the cross arm is placed outside, so that the requirement on the rotating space can be reduced, and the size of the cross arm is required to be longer.

Acquisition equipment with translational structure for acquisition area moving device

In addition to the above-described rotating structure, the image pickup device 4 can move in a linear trajectory relative to the object. As shown in fig. 5, for example, the image capturing device is located on a linear track, and sequentially passes through the target object along the linear track to capture images, and the image capturing device is not rotated during the process. Wherein the linear track can also be replaced by a linear cantilever. More preferably, the image capturing device is rotated to a certain degree when moving along a linear track, so that the optical axis of the image capturing device faces the target object.

Collection equipment with random motion structure as collection area moving device

In some cases, the movement of the capturing area is irregular, and as shown in fig. 6, for example, the image capturing device 4 may be held by hand to capture a picture around the target object, and at this time, it is difficult to move in a strict track, and the movement trajectory of the image capturing device is difficult to predict accurately. Therefore, in this case, how to ensure that the captured images can be accurately and stably synthesized into the 3D model is a difficult problem, which has not been mentioned yet. A more common approach is to take multiple photographs, with redundancy in the number of photographs to address this problem. However, the synthesis results are not stable. Although there are some ways to improve the composite effect by limiting the rotation angle of the camera, in practice, the user is not sensitive to the angle, and even if the preferred angle is given, the user is difficult to operate in the case of hand-held shooting. Therefore, the invention provides a method for improving the synthesis effect and shortening the synthesis time by limiting the moving distance of the camera for twice photographing.

For example, in the process of face recognition, a user can hold the mobile terminal to shoot around the face of the user in a moving mode. As long as the experience requirements (specifically described below) of the photographing position are met, the 3D model of the face can be accurately synthesized, and at this time, the face recognition can be realized by comparing with the standard model stored in advance. For example, the handset may be unlocked, or payment verification may be performed.

In the case of irregular movement, a sensor may be provided in the mobile terminal or the image acquisition device, and a linear distance moved by the image acquisition device during two times of photographing may be measured by the sensor, and when the moving distance does not satisfy the above-mentioned experience condition with respect to L (specifically, the following condition), an alarm may be issued to the user. The alarm comprises sounding or lighting an alarm to the user. Of course, the distance of the movement of the user and the maximum movable distance L may also be displayed on the screen of the mobile phone or prompted by voice in real time when the user moves the image acquisition device. The sensor that accomplishes this function includes: a range finder, a gyroscope, an accelerometer, a positioning sensor, and/or combinations thereof.

Collection equipment with multiple cameras

It can be understood that, in addition to the camera moving relative to the object to make the camera shoot the images of the object at different angles, a plurality of cameras can be arranged at different positions around the object, as shown in fig. 7, so that the images of the object at different angles can be shot simultaneously.

Image acquisition device position optimization

If when the outer surface information of the vase is collected, the image collecting device can rotate around the vase for a circle to shoot images within 360 degrees of the circumference of the vase. At this time, it is necessary to optimize at which position the image acquisition device acquires, otherwise it is difficult to consider both the time and the effect of 3D model construction. Of course, besides the mode of rotating around the target, a plurality of image capturing devices may be arranged to capture images simultaneously (specifically, refer to "multi-camera mode capturing apparatus"), and the position of the image capturing device still needs to be optimized, and the experience condition of the optimization is consistent with the above, and at this time, because of the plurality of image capturing devices, the optimized position is the position between two adjacent image capturing devices.

Gather regional mobile device and be rotating structure, image acquisition device rotates around the target object, when carrying out 3D and gather, image acquisition device changes for the target object in different collection position optical axis directions, and two adjacent image acquisition device's position this moment, or two adjacent collection positions of image acquisition device satisfy following condition:

δ<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.

When the image acquisition device is at any one of the two positions, the distance from the photosensitive element to the surface of the target object along the optical axis is taken as T. In addition to this method, in anotherIn one case, L is A_n、A_n+1Linear distance between optical centers of two image capturing devices, and A_n、A_n+1Two image acquisition devices adjacent to each other_n-1、A_n+2Two image acquisition devices and A_n、A_n+1The distances from the respective photosensitive elements of the two image acquisition devices to the surface of the target object 1 along the optical axis are respectively T_n-1、T_n、T_n+1、T_n+2，T＝(T_n-1+T_n+T_n+1+T_n+2)/4. Of course, the average value may be calculated by using more positions than the adjacent 4 positions.

As mentioned above, L should be a straight-line distance between the optical centers of the two image capturing devices, but since the optical center position 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 axial center of the connection between the image capturing device and the pan/tilt head (or platform, support), and the center of the proximal or distal surface of the lens may be used in some cases instead, and the error caused by the displacement is found to be within an acceptable range through experiments, and therefore the above range is also within the protection scope of the present invention.

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.

From the above experimental results and a lot of experimental experiences, it can be found that the value of δ should satisfy δ <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 of δ satisfies δ <0.498, the balance between the synthesis effect and the synthesis time can be optimally satisfied; delta <0.356 can be chosen for better synthesis, where the synthesis time is increased but the synthesis quality is better. Of course, to further enhance the synthesis effect, δ <0.311 may be selected. When the delta is 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. The above data are obtained by performing location optimization, and are not directed to others.

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 is data obtained when the image of the outer surface of the target is collected and 3D synthesized, and according to the above similar method, experiments on the inner surface of the target and the connection portion of the target can be performed, and corresponding data can be obtained as follows:

when the inner surface is acquired, the value of δ should satisfy δ <0.587, and the partial 3D model can be synthesized, and although some parts cannot be automatically synthesized, the method is acceptable under the condition of low requirement, and the parts which cannot be synthesized can be compensated manually or by replacing an algorithm. Particularly, when the value of δ satisfies δ <0.443, the balance between the synthesis effect and the synthesis time can be optimally taken into consideration; δ <0.319 can be chosen for better synthesis, where the synthesis time increases but the synthesis quality is better. Of course, δ <0.282 may be chosen to further enhance the synthesis. Whereas, when δ is 0.675, 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.

When the connecting part is collected, the value of delta is required to meet the condition that delta is less than 0.513, at this time, partial 3D models can be synthesized by matching with the images of the inner surface and the outer surface to form a complete 3D model comprising the inner surface and the outer surface, although a part of the 3D models cannot be automatically synthesized, the 3D model is acceptable under the condition of low requirement, and the part which cannot be synthesized can be compensated manually or by replacing an algorithm. Particularly, when the value of δ satisfies δ <0.415, the balance between the synthesis effect and the synthesis time can be optimally taken into consideration; δ <0.301 can be chosen for better synthesis, where the synthesis time increases but the synthesis quality is better. Of course to further enhance the synthesis effect δ <0.269 may be chosen. Whereas, when δ is 0.660, 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.

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.

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.

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.

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

1. A3D information synthesis image matching method based on image sorting is characterized in that:

step 1: determining a source image to be matched;

step 2: screening images adjacent to the source image;

and step 3: matching calculation is carried out on the screened images;

2. The method of claim 1, wherein: in step 2, the method of calculating the position Pi (Xi, Yi) at an arbitrary photographing time is:

the image acquisition device shoots a circle at any exposure time, and the arc length of the image acquisition device sliding is

To obtain the angle between the photographing position of the image acquisition device and the X axis

The position Xi ═ r × cos (n) at the exposure time Pi of an arbitrary image acquisition device position taken one round, and Yi ═ r × sin (n) are obtained.

3. The method of claim 1, wherein: the distance between adjacent acquisition positions of the two images is as follows:

4. The method of claim 3, wherein: δ < 0.603.

5. The method of claim 3, wherein: δ < 0.498.

6. The method of claim 3, wherein: δ < 0.356.

7. The method of claim 3, wherein: δ < 0.311.

8. The method of any of claims 1-7, wherein: the method further comprises the following steps:

carrying out image enhancement processing on the screened image;

9. A method for generating a physical object using three-dimensional model data, comprising the matching method according to any one of claims 1 to 8.

10. A method of constructing a three-dimensional model, comprising the matching method of any one of claims 1 to 8.

11. A method of three-dimensional data alignment comprising the matching method of any one of claims 1-8.