CN107170039A - The generation method of human body three-dimensional data model libraries - Google Patents
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Abstract
The invention belongs to DATA REASONING field, and in particular to a kind of generation method of human body three-dimensional data model libraries, the inventive method solves the problem of existing database data are less perfect, acquisition range is small, collection is single, data accuracy is poor.This method comprises the following steps：Step 1) set up human body three-dimensional virtual model library：Step 2) weight and offset of dummy model figure's label are obtained by study：Step 3) obtain PCA dominant eigenvalues：Step 4) computation model data：The inventive method low cost, expeditiously high accuracy, structure somatic data storehouse, are substantially shorter time of measuring, improve measurement accuracy and efficiency, and the build trend of human body can be estimated.
Description
Technical field
The invention belongs to DATA REASONING field, and in particular to a kind of generation method of human body three-dimensional data model libraries.
Background technology
With the development of technology, it is required in many industries to obtain three-dimensional human body measurement data.For example in medical domain
It is used for the change for the body size that monitoring occurs due to kidney failure or assesses the change of human body image to ensure operation
Success.Furthermore it is possible to which it is also, accurate human dimension beneficial to apparel industry to generate accurate physical trait and measurement data
Dress manufacturer or zero pin business and Communication with Customer contact are would be beneficial for, is beneficial to on-line purchase.
Body measurement system is to realize customized electronics, human somatotype Database element task and crucial skill
Art.Substantially it experienced in the evolution of more than 30 years from hand dipping to computer aided measurement, from contact type measurement to non-
Contact type measurement, from two dimension to three-dimensional develop 3 stages, and to automatic measurement and using computer measurement, processing and point
The direction of analysis is developed.Traditional anthropological measuring typically refers to survey tool and human contact, partes corporis humani when directly measuring static state
The height of position, width, girth equidimension, main survey tool are tape, bevel protractor, altimeter, range-viewfinder and slide meter etc..It is non-to connect
Touch measurement is main based on contemporary optics, melts the sections such as photoelectronics, computer graphics, information processing, computer vision
The e measurement technology that technology is integrated, it is when measuring measurand, image as detection and the means or load of transmission information
Body is used, and the purpose is to useful information is extracted from image.Existing body-scanner and anthropological measuring equipment are relied on
The 3-D view of high-resolution depth transducer, the optical mode of fixation or known camera angle next life adult body, this skill
Art needs professional equipment and high burden is showed to user, and user needs special center to carry out this people's body examination
Amount.Three-dimensional human body measurement method of the prior art has stereophotogrammetric method, laser optical method, Moire fringe mensuration etc..
Although three-dimensional human body measurement method can obtain somatic data, but still come with some shortcomings：
1. expensive scanning device, professional knowledge and special environment configurations are needed using 3-D scanning method.There is provided
The universal method and equipment of this measurement be not only bulky and costliness, and need to use complexity can determine related object
To the pixel depth or the detector device of distance of reference point.
2. using the measurement of the scanning systems such as laser and white light, it is necessary to which the measured is undressed, it easily exposes the measured
Privacy, and require that the measured keeps constant posture within a few minutes.In addition, laser can cause the measured psychological pressure,
The poor, laser of experience of people also easily injures the eyes of tested person.
3. based on height and weight and descriptive vocabulary prediction measurement data：Such method, which is characterized by, utilizes illustrative
Vocabulary gives human body marking, the shortcoming of this method be user using trouble, it is necessary to be provided for the build of oneself after corresponding scoring
The threedimensional model of reconstruction can be obtained.
4. recovering threedimensional model based on positive side photo, and measurement data is recovered by threedimensional model：This method it is main
Advantage is to input simply, it is only necessary to which front and side photo, it is possible to obtain, the threedimensional model of reconstruction, specific method are first
By calculating the human body attitude in picture, then rule of thumb manikin storehouse, goes to match corresponding posture, passes through two pictures
Constraint, obtain final threedimensional model.The shortcoming of this method is that the threedimensional model and true model of acquisition lack real chi
Very little mapping, and the posture that with only of the constraint of two pictures enters row constraint, it is difficult to precisely recover true figure's number of human body
According to.
5. there is the database of oneself different collections in each research institution, but these databases are all less perfect, gather model
Enclose small, gather single, not to the collection of sex and all age group, height section etc., without generality and generally applicable
Property.Therefore, current researcher can only be gone based on each different research purpose and interest from appropriate database or from
Own gathered data, therefore in the urgent need to setting up a comprehensive and wide variety of database.
The content of the invention
To solve, existing database data are less perfect, acquisition range is small, collection is single, data accuracy is poor asks
Topic, the invention provides a kind of test process is simple, acquisition range is wide and the accurate human body three-dimensional data model libraries of test data
Generation method.
Technical proposal that the invention solves the above-mentioned problems is：
The generation method of human body three-dimensional data model libraries, comprises the following steps,
Step 1) set up human body three-dimensional virtual model library：The standard appearance of generation arbitrary size corresponding with true measurement data
Gesture human body three-dimensional virtual model library；The virtual model library is the matrix of m × n × 3, and wherein m is model quantity, and n is number of vertex
Amount；The height of the standard gestures human body of the arbitrary size is 1.2 meters to 2.2 meters；
Step 2) weight and offset of dummy model figure's label are obtained by study：Using linear regression formula Y0=
W0XIt is known+b0, make Y0And YActual measurementDeviation is minimum, and study obtains WLearnAnd bLearn；Wherein：
Y0For the measurement data calculated；
XIt is knownFor known vector [height, weight, label0, label1 ..., labelN], wherein label0 ...,
LabelN is figure's label；
YActual measurementFor actual measurement data；
W0To need the weight learnt；
b0To need the offset learnt；
WLearnTo learn obtained weight；
bLearnTo learn obtained offset；
Step 3) obtain PCA dominant eigenvalues：
PCA characteristic values are predicted according to the following formula, are used as linear regression model (LRM) vector：
Y=WLearnX+bLearn
Y is the vector finally predicted；
X is the vector [height, weight, label0, label1 ..., labelN] of model actual measurement, wherein
Label0 ..., labelN are figure's label；
Again from the PCA characteristic values predicted, take the big preceding n value of characteristic value as PCA dominant eigenvalues, wherein n is more than 1
Natural number；
Step 4) computation model data：According to step 3) in obtain linear regression model (LRM) vector and PCA dominant eigenvalues,
Backwards calculation goes out vertex data, adds the topology information of existing model, and counter to release model data, foundation meets virtual photograph requirement
Human 3d model storehouse.
Further, the step 1) concretely comprise the following steps：Using Human Modeling software, had according to archetype generation
The basic model of certain topological structure, the standard appearance of arbitrary size corresponding with true measurement data is generated further according to basic model
Gesture human body three-dimensional virtual model library；The archetype includes real human body or the model manually set up.This step can facilitate
Find the corresponding relation between model position, and the error amount of preservation model before and after the processing.
Further, the step 1) also including the step of revision to basic model：The posture of judgement basis model
Whether standard gestures are met；If not meeting, automatic measurement is carried out to archetype and the measurement at tested position before deformation is recorded
Data, then by the gesture distortion of basic model be standard gestures, re-start automatic measurement and record position is tested after deformation
Measurement data.The step of revision.This step can exclude influence of the posture to measurement result, can improve model library data
Accuracy.
Further, the step 3) also include unified model position the step of：According to PCA characteristic values predict the outcome with very
The difference of actual measured amount result is averaged and variance, and choosing n values according to statistical result make it that average and variance are minimum.This step can be with
Exclude extreme case, optimization storage.
Further, the span 8 to 20 of the n.
Further, it is additionally included in step 4) addition random noise model on the basis of the human 3d model storehouse set up
Step 5)：Step 5) in the circle that model origin is randomly distributed in that radius is 1-6cm at grade, to measurement data
It is adjusted, generates random noise model, uncontrollable Small variables during actually being taken pictures with simulation；The random noise
Including position random noise and posture noise.Step 5) accuracy of model library measurement data can be improved.This step causes mould
Type noise resisting ability is stronger, and applicability is stronger.
Further, the radius of the circle is 4cm.
Further, the step 6 of optimization human body three-dimensional data model libraries is included)：
6.1) virtual photograph, obtains binary map；
6.2) neutral net of figure's label is trained using binary map；
6.3) neutral net of figure's label, the human body three after generation optimization are optimized using a small amount of real body measurement data
D Data Model storehouse.
Further, figure's label specifically includes chest waist ratio, stern height ratio, chest height ratio, the high ratio of waist, thigh circumference
Stern ratio and/or shoulder breadth chest ratio；The measurement data includes bust, waistline, hip circumference, shoulder breadth, brachium, leg length, upper-arm circumference, thigh
Enclose, calf circumference, body weight and/or height.
Further, the characteristic value and linear regression model (LRM) vector are that matrix is obtained using SVD decomposition methods.Can
Expeditiously to build somatic data storehouse
Further, the standard of arbitrary size corresponding with true measurement data is generated using open source software make-human
Posture human body three-dimensional virtual model library.
Advantages of the present invention is：
1. this method is inexpensive, high-precision, expeditiously build somatic data storehouse.Time of measuring is substantially shorter, is improved
Measurement accuracy and efficiency, and the build trend of human body can be estimated.The data that database degree is collected, data volume is big,
Classification is clear, the data of data category needed for can intuitively being found out by database interface, convenient analysis and research.
2nd, by carry out person recognition while data storage expand data volume, it is to avoid data redundancy.
3. the input data of the inventive method is easily obtained, it is only necessary to obtain pictorial information, it is possible to solve existing algorithm
Input it is complicated the problem of；Test equipment is simple, only needs simple equipment just to complete to input information gathering, data acquisition into
This is small.
4. optimum solution is obtained present invention employs the strategy that gradient declines, so as to improve the accuracy rate of prediction data.
5. set up Anthropometric Database.Custom made clothing service, external connection e-commerce system can be realized, it is possible to achieve
Line is sold.Human body surface image can also be drawn out by the 3D modeling software in server, be conducive to human somatotype research, doctor
The technological innovation of the technical fields such as, ergonomics and safety check.
Brief description of the drawings
Fig. 1 is facilities and equipments schematic diagram of the present invention；
Fig. 2 is the inventive method flow chart；
Fig. 3 is the flow chart of the inventive method distortion correction；
Fig. 4 is the prediction flow chart that depth convolutional neural networks label simplifies；
Fig. 5 is depth convolutional neural networks Tag Estimation flow chart；
Fig. 6 is the flow chart that model library is generated；
Fig. 7 is arbitrary size manikin figure；
Model deformation figure based on Fig. 8.
Embodiment
Refer to height, the body using two non-depth pictures and human body to be measured to the method that anthropometric data is predicted
The method that weight information is predicted to the measurement data of human body to be measured.
As shown in figure 1, realizing the equipment of the inventive method includes weight meter, ultrasound height instrument and 150 degree of wide-angles
The camera of 5000000 pixels.
As shown in Fig. 2 to 8, the method being predicted to anthropometric data comprises the following steps：
One) model library is built；The model library by multiple manikins and the multiple positions of each manikin measurement number
According to composition, build model library and comprise the following steps,
Step 1) set up human body three-dimensional virtual model library：The standard appearance of generation arbitrary size corresponding with true measurement data
Gesture human body three-dimensional virtual model library；The virtual model library is the matrix of m × n × 3, and wherein m is model quantity, and n is number of vertex
Amount；The height of the standard gestures human body of the arbitrary size is 1.2 meters to 2.2 meters；
Concretely comprise the following steps：The mark of arbitrary size corresponding with true measurement data is generated using open source software make-human
Quasi- posture human body three-dimensional virtual model library, generates the basic model with certain topological structure, further according to base according to archetype
Plinth model generates the standard gestures human body three-dimensional virtual model library of arbitrary size corresponding with true measurement data；The original mould
Type includes real human body or the model manually set up；Standard gestures refer to national standard《GBT 23698-2009 3-D scanning people
The general requirement of bulk measurement method》；All models are all met as same set of topological structure inside virtual data base, it is meant that be all
Same model deformation is past, can so obtain the principal component analysis information of the three-dimensional vertices of model in itself；
The step 1) also including the step of revision to basic model：Whether the posture of judgement basis model meets mark
Quasi- posture；If not meeting, automatic measurement is carried out to archetype and the measurement data at tested position before deformation is recorded, then by base
The gesture distortion of plinth model is standard gestures, re-starts automatic measurement and records the measurement data that position is tested after deformation.
Position where automatic measurement is carried out to basic model and is recorded, measurement position is recovered after basic model is deformed,
Re-start automatic measurement；The basic model is the undeformed model of acquiescence of certain topological structure standard gestures；Utilize void
Analog model is all the characteristics of same model is deformed and come, and can be taken in basic model measurement, the position where record, after deformation
That recovers that measurement position remeasures completes；
Step 2) weight and offset of dummy model figure's label are obtained by study：
Using linear regression formula Y0=W0XIt is known+b0, make Y0And YActual measurementDeviation is minimum, and study obtains WLearnAnd bLearn；Wherein：
Y0For the measurement data calculated；
XIt is knownFor known vector [height, weight, label0, label1 ..., labelN], wherein label0 ...,
LabelN is figure's label；
YActual measurementFor actual measurement data；
W0To need the weight learnt；
b0To need the offset learnt；
WLearnTo learn obtained weight；
bLearnTo learn obtained offset；
Step 3) obtain PCA dominant eigenvalues：
PCA characteristic values are predicted according to the following formula, are used as linear regression model (LRM) vector：
Y=WLearnX+bLearn
Y is the vector finally predicted；
X is the vector [height, weight, label0, label1 ..., labelN] of model actual measurement, wherein
Label0 ..., labelN are figure's label；
Again from the PCA characteristic values predicted, take the big preceding n value of characteristic value as PCA dominant eigenvalues, wherein n is more than 1
Natural number；
The step 3) also include unified model position the step of：Predicted the outcome and tied with truly measurement according to PCA characteristic values
The difference of fruit is averaging and variance, and choosing n values according to statistical result make it that average and variance are minimum, the span one of the n
As be 8 to 20.
Step 4) computation model data：
According to the linear regression model (LRM) vector and PCA dominant eigenvalues obtained in step 3, backwards calculation goes out vertex data,
It is counter to release model data plus the topology information of existing model, set up the human 3d model storehouse for meeting virtual photograph requirement.Mould
Type data mean error is within 1cm, and every hundred models of number that absolute value error is more than 2cm are not more than 1.
Be additionally included in step 4) set up human 3d model storehouse on the basis of add random noise model step 5, step
It is rapid 5) to be randomly distributed in radius at grade in model origin in 1-6cm circle, to be adjusted to measurement data,
Random noise model is generated, uncontrollable Small variables during actually being taken pictures with simulation；The random noise includes position
Random noise and posture noise.The preferred radius of circle is 4cm.
Also include the step 6 of optimization database)：
6.1) virtual photograph, obtains binary map；
6.2) neutral net of figure's label is trained using binary map；
6.3) neutral net of figure's label, the human body three after generation optimization are optimized using a small amount of real body measurement data
D Data Model storehouse.
Two) statistical value of the different parts ratio of manikin in model library is classified, generates figure's label；
Three) height, body weight and the photographic intelligence for including position to be measured of human body to be measured are obtained；The photographic intelligence bag
Human body front is included to shine and/or people's body side surface photograph；Photographic intelligence can be obtained by the video camera containing wide-angle lens, be entered to photo
Also include distortion correction step before row pretreatment；
Four) photo disposal is turned into binary map；
Five) according to constructed model library, depth convolutional neural networks study is carried out to binary map；(for example here can be by
6 grades of the ratio of the bust waistline of personage point, then can go out the label level of current human body to be measured according to the picture prediction of input here
Not)；Depth convolutional neural networks method is mainly by constructing multilayer convolutional network, using the method for semi-supervised learning, machine
Automatic learning characteristic, and classified；In Fig. 5,7 values of output represent the probability that the data belong to 7 different label grades.
Six) figure's label of human body to be measured is predicted according to learning outcome；
Seven) using height, body weight and the figure's label predicted, measurement data (bust, waist of human body to be measured are predicted
Enclose, hip circumference, thigh circumference, upper-arm circumference etc.).
Eight) the step of also including setting up human 3d model to be measured according to measurement data.
Figure's label includes the high ratio of chest waist ratio, stern height ratio, chest height ratio, waist, thigh circumference stern ratio and/or shoulder breadth
Chest ratio；The measurement data includes bust, waistline, hip circumference, shoulder breadth, brachium, leg length, upper-arm circumference, thigh circumference, calf circumference, body weight
And/or height.
The characteristic value and characteristic vector are that matrix is obtained using SVD decomposition methods；
Step 2) in formula can obtain optimum solution using gradient descent method.
Linear regression formula can have a variety of, and the formula that the present invention is used is adapted to use during low volume data, and data volume greatly may be used
With other established model.
Figure's label is designed：
By taking male's label as an example (women label is basically identical, and simply span is different), current male's label has 4,
Respectively chest waist ratio (bust_waist), chest height ratio (bust_height), waist height is than (waist_height), stern height
Than (hip_height).Figure's label can also expand dimensional information as needed.
Following label design is generated after being distributed according to existing statistics of database.
Chest waist ratio (bust_waist) design is as follows：
Stern height ratio (hip_height) design is as follows：
Chest height ratio (bust_height)
Waist height is than (waist_height)
Claims (10)
1. the generation method of human body three-dimensional data model libraries, it is characterised in that：Comprise the following steps,
Step 1) set up human body three-dimensional virtual model library：
The standard gestures human body three-dimensional virtual model library of generation arbitrary size corresponding with true measurement data；The dummy model
Storehouse is the matrix of m × n × 3, and wherein m is model quantity, and n is summit quantity；The body of the standard gestures human body of the arbitrary size
A height of 1.2 meters to 2.2 meters；
Step 2) weight and offset of dummy model figure's label are obtained by study：
Using linear regression formula Y0=W0XIt is known+b0, make Y0And YActual measurementDeviation is minimum, and study obtains WLearnAnd bLearn；Wherein：
Y0For the measurement data calculated；
XIt is knownFor known vector [height, weight, label0, label1 ..., labelN], wherein label0 ..., labelN
For figure's label；
YActual measurementFor actual measurement data；
W0To need the weight learnt；
b0To need the offset learnt；
WLearnTo learn obtained weight；
bLearnTo learn obtained offset；
Step 3) obtain PCA dominant eigenvalues：
PCA characteristic values are predicted according to the following formula, are used as linear regression model (LRM) vector：
Y=WLearnX+bLearn
Y is the vector finally predicted；
X be model actual measurement vector [height, weight, label0, label1 ..., labelN], wherein label0 ...,
LabelN is figure's label；
Again from the PCA characteristic values predicted, take the big preceding n value of characteristic value as PCA dominant eigenvalues, wherein n is oneself more than 1
So count；
Step 4) computation model data：
According to step 3) in obtained linear regression model (LRM) vector and PCA dominant eigenvalues, backwards calculation goes out vertex data, adds
The topology information of existing model, it is counter to release model data, set up the human 3d model storehouse for meeting virtual photograph requirement.
2. the generation method of human body three-dimensional data model libraries according to claim 1, it is characterised in that：The step 1)
Concretely comprise the following steps：
Using Human Modeling software, the basic model with certain topological structure is generated according to archetype, further according to basic mould
Type generates the standard gestures human body three-dimensional virtual model library of arbitrary size corresponding with true measurement data；The archetype bag
The model for including real human body or manually setting up.
3. the generation method of human body three-dimensional data model libraries according to claim 2, it is characterised in that：The step 1) also
The step of including being revised to basic model,
Whether the posture of judgement basis model meets standard gestures；If not meeting, automatic measurement is carried out to archetype and is remembered
The measurement data at tested position before record deformation, then by the gesture distortion of basic model be standard gestures, re-start automatic measurement
And record the measurement data that position is tested after deformation.
4. the generation method of human body three-dimensional data model libraries according to claim 1, it is characterised in that：The step 3) also
The step of including unified model position：
Predicted the outcome according to PCA characteristic values and the difference of true measurement is averaged and variance, n values are chosen according to statistical result
So that average and variance are minimum.
5. the generation method of human body three-dimensional data model libraries according to claim 4, it is characterised in that：The value of the n
Scope 8 to 20.
6. the generation method of human body three-dimensional data model libraries according to claim 1, it is characterised in that be additionally included in step
4) step 5 of random noise model is added on the basis of the human 3d model storehouse set up),
Step 5) in the circle that model origin is randomly distributed in that radius is 1-6cm at grade, measurement data is entered
Row adjustment, generates random noise model, uncontrollable Small variables during actually being taken pictures with simulation；The random noise bag
Include position random noise and posture noise.
7. the generation method of human body three-dimensional data model libraries according to claim 6, it is characterised in that：The half of the circle
Footpath is 4cm.
8. according to the generation method of any described human body three-dimensional data model libraries of claim 1 to 7, it is characterised in that also
Step 6 including optimizing human body three-dimensional data model libraries)：
Step 6)
6.1) virtual photograph, obtains binary map；
6.2) neutral net of figure's label is trained using binary map；
6.3) neutral net of figure's label, the human body three-dimensional number after generation optimization are optimized using a small amount of real body measurement data
According to model library.
9. the generation method of human body three-dimensional data model libraries according to claim 8, it is characterised in that：Figure's label
Including chest waist ratio, stern height ratio, chest height ratio, the high ratio of waist, thigh circumference stern ratio and/or shoulder breadth chest ratio；The measurement data bag
Include bust, waistline, hip circumference, shoulder breadth, brachium, leg length, upper-arm circumference, thigh circumference, calf circumference, body weight and/or height.
10. the generation method of human body three-dimensional data model libraries according to claim 9, it is characterised in that：The characteristic value
It is that matrix is obtained using SVD decomposition methods with linear regression model (LRM) vector.
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| CN110378291A (en) * | 2019-07-22 | 2019-10-25 | 浙江大学 | A kind of characteristics of human body's parameter prediction method based on semi-supervised learning |
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| CN108389213A (en) * | 2018-01-24 | 2018-08-10 | 上海工程技术大学 | A kind of adhesion girth meaurement method based on section point cloud |
| CN108389213B (en) * | 2018-01-24 | 2021-06-11 | 上海工程技术大学 | Adhesion circumference measuring method based on tangent plane point cloud |
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| CN109409348A (en) * | 2018-12-29 | 2019-03-01 | 北京卡路里信息技术有限公司 | A kind of determination method, apparatus, equipment and the storage medium of user's sign |
| CN110378291A (en) * | 2019-07-22 | 2019-10-25 | 浙江大学 | A kind of characteristics of human body's parameter prediction method based on semi-supervised learning |
| CN112001413A (en) * | 2020-07-13 | 2020-11-27 | 中国标准化研究院 | A body shape data prediction system based on human body size database |
| CN112001413B (en) * | 2020-07-13 | 2023-06-06 | 中国标准化研究院 | A human body shape data prediction system based on human body size database |
| CN117021068A (en) * | 2023-06-28 | 2023-11-10 | 哈尔滨工程大学 | Bionic robot control logic acquisition method based on artificial intelligence |
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