CN109147051B - Human body 3D modeling method based on mobile phone scanning - Google Patents
Human body 3D modeling method based on mobile phone scanning Download PDFInfo
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Abstract
The invention discloses a human body 3D modeling method based on mobile phone scanning, which is applied to a mobile phone and comprises the following steps: s1, generating a TSDF 3D grid model; s2, dense image calibration S3: and selecting key frames S4: dense image matching, generating a depth image S5: and updating the volume mesh model S6, generating a human body 2D image on a mobile phone screen. The invention utilizes the camera of the mobile phone to carry out three-dimensional modeling on the human body, does not need any additional equipment and simultaneously utilizes the computing power of the mobile phone to complete the three-dimensional modeling computing work of the human body; by using the human body symmetry principle, the system can automatically generate the three-dimensional of the whole human body as long as a half body is scanned; the scanning time of a user is greatly saved, and when the user holds the mobile phone with one hand, the scanning is inconvenient and cannot be influenced; no need of customer to fill in any data, simple operation and low cost.
Description
Technical Field
The invention belongs to the field of 3D modeling, and particularly relates to a human body 3D modeling method based on mobile phone scanning.
Background
Building a three-dimensional (3D) model for a human body in the field of clothing is a basis for application of three-dimensional virtual fitting, three-dimensional Virtual Reality (VR) and the like.
At present, the method is mainly realized by the following methods in China.
The first method comprises the following steps: three-dimensional (3D) software modeling.
And the second method comprises the following steps: three-dimensional (3D) scan modeling.
And the third is that: model substitution.
The first type of three-dimensional (3D) software for building a three-dimensional model of the human body requires the use of 3D software tools to modify the model. It is difficult to automatically obtain a three-dimensional model conforming to the body shape of an individual subject in a short time. Because the time is too long, the user waits too long, so the modeling method is difficult to be popularized and commercialized in the application of virtual fitting on the line of the clothes and the like.
The second utilizes three-dimensional (3D) scan modeling. A specialized 3D scanning device is required for scanning. The model data obtained in the mode most accurately reflects the body type of the measured object. But requires the support of expensive and bulky 3D scanner equipment. The most typical commercial (three-dimensional modeling of the human body for virtual fitting) is the Mirror Display. The method is characterized in that a plurality of sensors and three-dimensional scanners are arranged in a large-scale simulation fitting mirror frame, when a user walks in front of the mirror, the user captures body figure data (three-dimensional scanning) to generate a three-dimensional human body model in real time, and then the three-dimensional human body model is used for virtual fitting in the next step. From this we can see that because the scanning device is too large. Can only be installed and used in a physical store in the field of clothes. Is not suitable for human body three-dimensional modeling required by virtual fitting on the internet at all. The use is greatly limited.
The third model replacement method (which is to use 3D software to perform model simulation) has three replacement modes: replacing one: a small number of fixed model replacements. Namely, several fixed models are used, and a user can select a fixed model which looks relatively close to the body shape of the user for replacement at the mobile terminal (the fixed model is modeled by 3D scanning in advance). Because the types of the selectable fixed models are limited, the requirements of different shapes of the masses are difficult to meet, and the consumers can hardly judge which model is close to the body type of the consumer, so that the fixed models are far from the real people after being replaced, which is also the reason that the fixed models cannot be popularized at present. And replacing two: the method is a model simulation method, which comprises the steps of collecting a large amount of human body data in advance by a computer system and establishing a human body database. (these entered mannequins have built three-dimensional mannequins). The user data is compared with the similar body form in the database, and then the head of the user is replaced to obtain the three-dimensional model. The method comprises the step of establishing a human body model by recording more than 1 ten thousand Chinese female human bodies in advance. These models are then built into a background body database. The user prepares the front photo of the user, fills in about 25 personal stature data (such as height, weight, three-dimensional circumference and the like and uploads the personal photos together, the personal three-dimensional human body model generated by replacing the target model closest to your in the human body database is found through the personal stature data information, although the fidelity of the method can be greatly improved, however, the user needs to fill in and input a large amount of personal stature data, some data users need to obtain the data through other methods, the user feels too complicated and troublesome, meanwhile, the provider also needs to spend a large amount of manpower and time to establish a huge human body database, the deformation of the third replacement and the second replacement is also a model simulation method, a large amount of human body models are recorded in 3D in advance to establish the human body database, human body joints in the database are spliced to a measured person human body model which is generated by approaching the appearance of the obtained measured person as much as possible after the limbs are digitalized, the deformation of the second replacement is the method used for realizing the three-dimensional modeling of the human body, the operation flow is to use a mobile phone to shoot two clear front photographs and side contour photographs at a certain distance, and to simply fill in a plurality of personal body data, then uploading the data together, using the obtained body data, the outline picture and the front picture by the background system, matching and splicing with the data in the database to generate the human body three-dimensional model of the measured person, although the number of items to be filled by the user is reduced, however, the other aspects are more disadvantageous, and in order to obtain a clearly usable outline picture, firstly, the photographer is required to take a certain distance when taking a picture.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides human body 3D modeling prevention based on mobile phone scanning.
The technical scheme of the invention is realized as follows: a human body 3D modeling method based on mobile phone scanning is applied to a mobile phone and comprises the following steps:
s1, generating a TSDF 3D grid model:
s1-1: estimating the position and the orientation of each time point of the mobile phone camera by adopting a depth reconstruction algorithm;
s1-2: based on a stereo matching algorithm, a group of images at different angles in the same scene are shot by a mobile phone camera to obtain a scene depth image;
s1-3: calculating the position and the orientation of the camera and the depth image obtained by the S1-1 and the S1-2 to obtain related point cloud data through coordinate conversion, wherein the point cloud data is obtained by recording scanning data in a point form, and each point contains three-dimensional coordinates and is used for representing the shape of the outer surface of an object;
s1-4: performing fusion processing on the obtained point cloud data by adopting a TSDF algorithm to obtain a TSDF 3D grid model;
s2, dense image calibration, namely tracking the mobile phone camera in a scanning scene by adopting a depth floating point reconstruction algorithm to align the position of the mobile phone camera with the position of the camera at the last time, obtaining a tracking result of the position of the mobile phone camera and obtaining a motion track of the mobile phone;
s3: selecting a key frame, namely after the motion trail of the mobile phone is obtained through S2, selecting a key frame from the motion trail of the mobile phone to compare with the current image, and generating the depth image of the current visual angle by adopting a stereo matching algorithm;
s4: dense image matching, namely, calculating the corresponding relation of each point on any two frames of images in the motion trail of the mobile phone and the relation between the shooting positions of the two frames of images, and generating an integral depth image through epipolar geometry;
s5: updating the volume grid model, namely importing the depth image of the current view angle obtained in the S2 and the S3 and the integral depth image into the volume grid model to update the TSDF 3D grid model to obtain depth image fusion of different view angles;
and S6, generating a 2D image, namely mapping the updated TSDF 3D grid model to the 2D current camera position of the mobile phone, and generating the 2D image on the mobile phone screen.
The key frame and the current image selected in the key frame selection step meet the following requirements:
the more the overlap part of the key frame and the current human body image is, the better the overlap part is; the remote background in the current human body image is not calculated; and (3) according to the human body symmetry principle, performing mirror symmetry processing on the current human body image, calculating half of the image, and copying the other half of the image.
Preferably, the mobile phone is additionally provided with an additional device, and the additional device is used for scanning and penetrating human clothes to scan and image a human body.
Preferably, the additional device is an infrared ray scanning section, an X-ray scanning section or a millimeter wave scanning section.
Preferably, the infrared scanning component, the X-ray scanning component and the millimeter wave scanning component are connected with the outside of the mobile phone through an external interface or the infrared scanning component, the X-ray scanning component and the millimeter wave scanning component are built in the mobile phone and connected with the mobile phone chip through electric signals.
The invention has the beneficial effects that: the invention utilizes the camera of the mobile phone to carry out three-dimensional modeling on the human body, does not need any additional equipment and simultaneously utilizes the computing power of the mobile phone to complete the three-dimensional modeling computing work of the human body; by using the human body symmetry principle, as long as half body is scanned, the system can automatically generate the three-dimensional of the whole human body; the scanning time of a user is greatly saved, and when the user holds the mobile phone with one hand, the scanning is inconvenient and cannot be influenced; no need of customer to fill in any data, simple operation and low cost.
Drawings
Fig. 1 is a method schematic diagram of a human body 3D modeling method based on mobile phone scanning.
Fig. 2 is a flowchart of steps of a human body 3D modeling method based on mobile phone scanning.
Detailed Description
The following further illustrates embodiments of the invention:
example one
As shown in fig. 1-2, a human body 3D modeling method based on mobile phone scanning is applied to a mobile phone, and the method comprises the following steps:
a human body 3D modeling method based on mobile phone scanning is applied to a mobile phone and comprises the following steps:
s1, generating a TSDF 3D grid model:
s1-1: estimating the position and the orientation of each time point of the mobile phone camera by adopting a depth reconstruction algorithm;
s1-2: based on a stereo matching algorithm, a group of images at different angles in the same scene are shot by a mobile phone camera to obtain a scene depth image;
s1-3: calculating the position and the orientation of the camera and the depth image obtained by S1-1 and S1-2 to obtain related point cloud data through coordinate transformation, wherein the point cloud data refers to scanning data recorded in a point form, and each point contains three-dimensional coordinates for representing the shape of the outer surface of an object;
s1-4: performing fusion processing on the obtained point cloud data by adopting a TSDF algorithm to obtain a TSDF 3D grid model;
the Depth Reconstruction in the step S1 is an existing algorithm.
A depth image is also called a distance image, and is an image in which a distance (depth) value from an image acquirer to each point in a scene is used as a pixel value. Which directly reflects the geometry of the visible surface of the scene.
TSDF is short for truncated signed distance function, which means Chinese to truncate signed distance function or truncated signed distance function.
The TSDF model divides the whole three-dimensional space to be reconstructed into grids, numerical values are stored in each grid, and the size of a median value in the grid model represents the distance between the grid and the reconstructed surface. The reconstructed surface is positive to one side of the camera and negative to the other. The absolute value is larger the farther the grid point is from the reconstructed surface. The crossing points from positive to negative in the mesh model represent the surface of the reconstructed scene. This method is used to update a value in each small grid. This value represents the closest distance of the mesh to the model surface, also known as the TSDF value. For each grid, the value of TSDF is updated and recorded in each frame, and then the reconstructed model is restored through the TSDF value. This method is commonly referred to as a volume based method. The core idea of this method is that by constantly updating and fusing (fusion) measurements of this type of TSDF, we can get closer and closer to the true value that is needed.
S2, dense image calibration, namely tracking the mobile phone camera in a scanning scene by adopting a depth floating point reconstruction algorithm to align the position of the mobile phone camera with the position of the last camera, obtaining a tracking result of the position of the mobile phone camera and obtaining a motion track of the mobile phone;
s3: selecting a key frame, namely after the motion trail of the mobile phone is obtained through S2, selecting a key frame from the motion trail of the mobile phone to compare with the current image, and generating the depth image of the current visual angle by adopting a stereo matching algorithm;
s4: calculating the corresponding relation of each point on any two frames of images in the motion trail of the mobile phone and the relation between the shooting positions of the two frames of images, and generating an integral depth image through epipolar geometry;
s5: updating the volume grid model, namely importing the depth image of the current view angle obtained in the S2 and the S3 and the integral depth image into the volume grid model to update the TSDF 3D grid model to obtain depth image fusion of different view angles;
and S6, generating a 2D image, namely mapping the updated TSDF 3D grid model to the 2D current camera position of the mobile phone, and generating the 2D image on the mobile phone screen.
Specifically, the key frame and the current image selected in the S3 key frame selection step satisfy the following requirements:
the more the overlap part of the key frame and the current human body image is, the better the overlap part is; calculating the background of a distant place in the current human body image, wherein the distant place refers to a scene which is 2 meters away from the human body in the human body image; and (3) according to the human body symmetry principle, performing mirror symmetry processing on the current human body image, calculating half of the image, and copying the other half of the image. Therefore, the calculation amount can be greatly saved, and the speed can be improved.
The stereo matching algorithm in the step S3 is mainly to estimate the parallax value of the pixel point by establishing an energy cost function and minimizing the energy cost function. The essence of the stereo matching algorithm is an optimization solution problem, and some constraints are added by establishing a reasonable energy function. The equation solution is carried out by adopting an optimization theory method, which is also a method for solving all pathological problems.
In addition, S4: dense image matching, generating Epipolar Geometry (Epipolar Geometry) in depth images means that two cameras shoot the same object at different positions, and if the scenes in the two pictures have overlapped parts, we reasonably believe that a certain correspondence exists between the two pictures. How to describe the correspondence between them, the description tool is an epipolar geometry. It is an important mathematical method for studying stereo vision.
The most direct method for searching the corresponding relation between two images is point-by-point matching, and if a certain constraint condition epipolar constraint (epipolar constraint) is added, the searching range can be greatly reduced.
The invention mainly relates to a multi-image imaging technology which utilizes a common mobile phone to carry out 3D scanning, wherein the multi-image imaging technology is similar to an imaging mechanism of human eyes.
What the invention is to do is to display the constructed 3D object on the 2D screen coordinates. For this process, the coordinate system transformation sequence is: object coordinate system-world coordinate system-camera coordinate system-projection coordinate system-image (pixel) coordinate system.
3D information acquired after mobile phone scanning is reduced to a (2-dimensional) projection coordinate system from a camera coordinate system and then converted to an image coordinate system (2D), and therefore the work that a constructed 3D object is displayed on a 2D screen coordinate is completed. In fact, 3D objects can be seen on a 2D screen by coordinate transformation.
Example two
The difference between the second embodiment and the first embodiment is that the mobile phone is additionally provided with an additional device, and the additional device is used for scanning and imaging the human body by scanning and penetrating the clothes of the human body.
In particular, the additional device is an infrared scanning component, an X-ray scanning component (also called backscatter scanning) or a millimeter wave scanning component.
Preferably, the infrared scanning component, the X-ray scanning component and the millimeter wave scanning component can be externally arranged on the mobile phone, like a mobile phone charging sleeve, or can be internally arranged, the built-in additional device is subjected to miniaturization processing firstly, and then is placed in the mobile phone by a mobile phone manufacturer after being miniaturized.
Specifically, when the mobile phone is externally connected, the infrared scanning component, the X-ray scanning component and the millimeter wave scanning component are externally connected with the mobile phone or the infrared scanning component through an external interface; when the mobile phone is internally provided, the X-ray scanning component and the millimeter wave scanning component are internally arranged in the mobile phone and are connected with the mobile phone chip through electric signals.
The infrared scanning component, the X-ray scanning component and the millimeter wave scanning component respectively adopt an infrared scanning imaging technology, an X-ray scanning imaging technology and a millimeter wave scanning imaging technology to scan, and belong to the application of three different scanning imaging technologies. They can be used in combination with the mobile phone 3d modeling respectively, and these imaging technologies can penetrate any clothes of the user to obtain clear human body contours (without being influenced by wearing clothes). The clear human body contour obtained by the technologies is combined with the mobile phone human body 3D modeling method, so that the influence of whether the three-dimensional human body modeling is tight and loose by a modeler wearing clothes is solved.
When the system is used, the camera of the mobile phone is turned on, the system firstly identifies whether the user wears a tight-fitting garment, if the user is judged to wear a tight-fitting garment, the system can obtain a satisfactory human body outline, and then the system automatically enters a 3d scanning modeling mode. Otherwise, the user is reminded to open the additional device, and the additional device is used for carrying out the mobile phone human body 3D modeling method to carry out the matched modeling.
Variations and modifications to the above-described embodiments may occur to those skilled in the art, which fall within the scope and spirit of the above description. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and modifications and variations of the present invention are also intended to fall within the scope of the appended claims. Furthermore, although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
Claims (5)
1. A human body 3D modeling method based on mobile phone scanning is characterized in that the method is applied to a mobile phone, and comprises the following steps:
s1, generating a TSDF 3D grid model:
s1-1: estimating the position and the orientation of each time point of the mobile phone camera by adopting a depth reconstruction algorithm;
s1-2: based on a stereo matching algorithm, a group of images at different angles in the same scene are shot by a mobile phone camera to obtain a scene depth image;
s1-3: obtaining related point cloud data by utilizing the camera position, orientation and depth image obtained by the S1-1 and the S1-2 through coordinate conversion, wherein the point cloud data is recorded by scanning data in a point form, and each point comprises a three-dimensional coordinate and is used for representing the shape of the outer surface of an object;
s1-4: performing fusion processing on the obtained point cloud data by adopting a TSDF algorithm to obtain a TSDF 3D grid model;
s2, dense image calibration, namely tracking the mobile phone camera in a scene of scanning a human body by adopting a depth floating point reconstruction algorithm to align the position of the mobile phone camera with the position of the last camera, obtaining a tracking result of the position of the mobile phone camera and obtaining a motion track of the mobile phone;
s3: selecting a key frame, namely after the motion trail of the mobile phone is obtained through S2, selecting a key frame from the motion trail of the mobile phone to compare with the current human body image, and generating a depth image of the current visual angle by adopting a stereo matching algorithm;
s4: calculating the corresponding relation of each point on any two frames of images in the motion trail of the mobile phone and the relation between the shooting positions of the two frames of images, and generating an integral depth image through epipolar geometry;
s5: updating the volume mesh model, namely importing the depth image of the current view angle obtained by the S2 and the S3 and the whole depth image into the TSDF 3D mesh model to update the TSDF 3D mesh model to obtain depth image fusion of different view angles;
and S6, generating a 2D image, namely mapping the updated TSDF 3D grid model to the 2D camera position of the current mobile phone, and generating a human body 2D image on the mobile phone screen.
2. The human body 3D modeling method based on mobile phone scanning as claimed in claim 1, wherein S3: the key frame and the current image selected in the key frame selection step satisfy the following requirements:
the more the overlapping part of the key frame and the current human body image is, the better the overlapping part is; the remote background in the current human body image is not calculated; and (4) according to the human body symmetry principle, performing mirror symmetry processing on the current human body image, calculating half of the image, and copying the other half of the image.
3. The human body 3D modeling method based on mobile phone scanning as claimed in claim 1, wherein the mobile phone is equipped with an additional device, and the additional device is used for scanning and imaging the human body by penetrating the human body clothes.
4. The mobile phone scanning-based human body 3D modeling method as claimed in claim 3, wherein the additional device is an infrared scanning component, an X-ray scanning component or a millimeter wave scanning component.
5. The human body 3D modeling method based on mobile phone scanning as claimed in claim 3, wherein the infrared ray scanning component, the X ray scanning component, the millimeter wave scanning component are connected with the outside of the mobile phone through an external interface or the infrared ray scanning component, the X ray scanning component, the millimeter wave scanning component are built inside the mobile phone and connected with the mobile phone chip through electric signals.
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| CN108846885A (en) * | 2018-06-06 | 2018-11-20 | 广东您好科技有限公司 | A kind of model activating technology based on 3-D scanning |
| CN108898630B (en) * | 2018-06-27 | 2020-12-15 | 清华-伯克利深圳学院筹备办公室 | Three-dimensional reconstruction method, device, equipment and storage medium |
| CN109241901B (en) * | 2018-08-30 | 2019-08-06 | 天津理工大学 | A kind of detection and recognition methods to the three-dimensional point cloud with hole |
| BR112021011113A2 (en) * | 2018-12-14 | 2021-08-31 | Spectral Md, Inc. | SYSTEM AND METHOD FOR FORMING A SPECTRAL IMAGE OF MULTIPLE HIGH PRECISION APERTURES |
| CN109816784B (en) * | 2019-02-25 | 2021-02-23 | 盾钰（上海）互联网科技有限公司 | Method and system for three-dimensional reconstruction of human body and medium |
| CN110006340B (en) * | 2019-03-26 | 2020-09-08 | 华为技术有限公司 | Object size measuring method and electronic equipment |
| CN112053435A (en) * | 2020-10-12 | 2020-12-08 | 武汉艾格美康复器材有限公司 | Self-adaptive real-time human body three-dimensional reconstruction method |
| CN112606929B (en) * | 2020-11-08 | 2021-09-14 | 山东格菱电动车有限公司 | Height lifting control system for vehicle seat |
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