CN100349189C - Sparse grid-oriented three-dimensional foot type fast acquiring method based on standard foot deformation - Google Patents

Sparse grid-oriented three-dimensional foot type fast acquiring method based on standard foot deformation Download PDF

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CN100349189C
CN100349189C CNB2005100612723A CN200510061272A CN100349189C CN 100349189 C CN100349189 C CN 100349189C CN B2005100612723 A CNB2005100612723 A CN B2005100612723A CN 200510061272 A CN200510061272 A CN 200510061272A CN 100349189 C CN100349189 C CN 100349189C
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pin
sparse grid
standard
foot
model
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CN1773555A (en
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潘云鹤
耿卫东
高飞
徐兴华
王毅刚
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Zhejiang University ZJU
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Zhejiang University ZJU
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Abstract

The present invention discloses a sparse grid-oriented three-dimensional foot type fast acquiring method based on standard foot deformation. The method comprises the steps that firstly, a standard foot type library is established; secondly, a three dimensional coordinate is established, and an input vector of the length of foot of tiptoe and heel points of a sparse grid foot is selected; thirdly, the vector of the length of foot in the second step is parallel to a Y-axis through rotating sparse grid foot models; fourthly, a standard foot model with length and width of foot, which are close to the sparse grid foot model is selected from a standard foot type library according to the information of the length and the width of the foot; fifthly, the posture and the position of the sparse grid foot models are adjusted, so that the sparse grid foot models are evenly distributed around the standard foot model; sixthly, corresponding points between the standard foot model and the sparse grid foot models are found; seventhly, control vertexes of the sparse grid foot models are remodified according to corresponding points, and the sparse grid foot models are rebuilt by new control vertexes, so a three-dimensional foot type data is obtained. The present invention can be used for rapidly obtaining a three-position data of the foot type and reducing motion noise, and has no requirement for the taken visual points, and result is matched well with the practice.

Description

Three-dimension foot type fast acquiring method towards sparse grid based on the distortion of standard pin
Technical field
The present invention relates to the fast modeling method of three-dimension foot type, relate in particular to a kind of three-dimension foot type fast acquiring method based on the distortion of standard pin towards sparse grid.
Background technology
With regard to the obtaining of pin type data, present mainstream technology approach is based on optical system, comprising:
1) based on the triangulation technique of laser.Be present three-dimensional data obtain manner all the fashion, the precision height, applied widely, but the costing an arm and a leg of equipment.Critical piece has the high-speed camera head, generating laser, signal processor, high precision stepper motor etc.
2) based on the measuring technique of structured light.Critical piece has special-purpose projector, and it is first-class to make a video recording.Precision is medium, and price is also medium.
3) based on the measuring technique of computer vision.Main equipment is camera, and its core is a stereo visual system.
These measuring techniques mainly by measuring all dense points at random (up to ten thousand), obtain the three-dimensional model of object being measured, and this method is very effective to the complete static object that does not have vitality.But for the measurement of vital object, because in measuring process, lived object can produce slight movement.The direct influence of this motion noise makes the user just need a large amount of aftertreatment time of cost, and seeming takes time and effort especially.We are according to the characteristics of human body pin type similarity, by setting up the standard reference model of people's pin, only need sparse net point (generally to be no more than 1,000 then, can obtain in moment, drop to the motion noise minimum), by the means of distortion, just can obtain the three-dimension foot pattern type of human body apace.
The distortion modeling method is one of common methods of computer-aided geometry moulding, is generally used for the modeling and the moulding of a certain class complex object.Its rudimentary algorithm framework is: on the one or more known three-dimensional model to a certain type objects, the user at first specifies him to want one group of locus constraint of this type objects of modeling, check then whether known three-dimensional model satisfies these constraints,, then directly return this object as satisfying; Otherwise, computer system is automatically according to the style characteristic and the domain knowledge of this type objects, by means such as geometric reasoning and constraint solvings, iteratively the geometry of known models and topology are changed and revise, satisfy up to results model till user's the appointment constraint, and return this results model.
Summary of the invention
The purpose of this invention is to provide a kind of three-dimension foot type fast acquiring method based on the distortion of standard pin towards sparse grid.
The step of method is as follows:
1) sets up standard pin type storehouse;
2) set up three-dimensional coordinate, select the tiptoe heel point structure pin long vector of the sparse grid pin of input;
3) rotation sparse grid pin model makes step 2) in the pin long vector parallel with Y-axis;
4) from standard pin type storehouse, choose the standard pin model that the wide information of the long pin of pin is close with it according to the wide information of the long pin of pin;
5) adjust the posture and the position of sparse grid pin model, make it to be evenly distributed on standard pin model around;
6) corresponding point between searching standard pin model and the sparse grid pin model;
7) remodify the control vertex of sparse grid pin model according to corresponding point, by new control vertex sparse grid pin model is rebuild, thereby obtained three-dimension foot type data.
The target of three-dimensional measurement of the present invention is the dense three-dimensional data model that obtains object to be measured.In the measuring process for vital object (as human body), consumed time is long at traditional measuring method, generally more than 10 minutes.Because lived object can produce slight movement, form the motion noise, make the user just need a large amount of aftertreatment time of cost, take time and effort especially.The method that we taked is, utilizes present measuring technique, and moment (less than 1 second) obtains the sparse grid of object to be measured, thereby drops to the motion noise minimum.According to the characteristics of human body pin type similarity,,, just can obtain the three-dimension foot pattern type of human body apace then by the means of distortion by setting up the standard reference model of people's pin.Our method is counted for obtained vision and is not required, this point is the assurance of obtaining results model fast, under the opposite extreme situations,, can use this method equally and be out of shape the final three-dimension foot type that obtains even only obtain two or three points above user's pin type.
Description of drawings
Fig. 1 is the standard pin three-dimensional model that the present invention selects;
Fig. 2 a is the sparse grid pin model of test case;
Fig. 2 b is the three-dimension foot type that is gone out by Fig. 2 a sparse grid pin model transferring;
Fig. 3 is the bounding box dividing method synoptic diagram of submethod in the step 6) of the present invention;
Fig. 4 is a software flow pattern of the present invention;
Fig. 5 is that the present invention seeks corresponding point substep software flow pattern;
Fig. 6 is that the present invention constructs new curved surface step software flow pattern;
Fig. 7 test flow chart of the present invention.
Embodiment
The acquisition methods of the sparse grid of three-dimension foot type is referring to the summary of the invention of application publication number CN 1544883 A.Behind the sparse grid that obtains the three-dimension foot type,,, be out of shape then if pin type standard of comparison to be measured is selected different age brackets, the standard pin of different sexes from standard pin storehouse with the classification of the pin among the crowd.
As shown in Figure 4, the step based on the three-dimension foot type fast acquiring method of standard pin distortion towards sparse grid is as follows:
1) set up a standard pin type storehouse: obtain three-dimension foot type in the pin type storehouse with the way of laser scanning, according to the different ages, sex, pin is long, chooses some comparatively regular people of pin type pin type respectively and scans, and forms pin type storehouse.
2) set up three-dimensional coordinate, select the sparse grid pin of input (to see Fig. 2 tiptoe heel point structure pin long vector (tiptoe vector-heel vector) a);
3) rotation sparse grid pin model makes step 2) in the pin long vector parallel with Y-axis, promptly the institute of sparse grid pin model is had a few enforcement and center on X respectively, Y, the rotation of Z axle is until step 2) in the pin long vector selected parallel with Y-axis;
4) from standard pin type storehouse, choose the standard pin model (see figure 1) that the wide information of the long pin of pin is close with it according to the wide information of the long pin of pin;
5) adjust the posture and the position of sparse grid pin model, make it to be evenly distributed on standard pin model around;
6) corresponding point between searching standard pin model and the sparse grid pin model;
7) remodify the control vertex of sparse grid pin model according to corresponding point, by new control vertex sparse grid pin model is rebuild, thereby obtained three-dimensional feature pin model (seeing Fig. 2 b).
The wide information of the long pin of the pin of described basis is chosen the standard pin model that the wide information of the long pin of pin is close with it from standard pin type storehouse: its step is
1) in standard pin type storehouse, travels through all standard pin type information, obtain the long and wide data of pin of pin of pin type in the storehouse;
2) after two parameters of the sparse grid pin model of and wide data of pin and input long with pin in the current storehouse are done respectively and differed from, get the absolute value of the product of two differences;
3), select in the storehouse of absolute value minimum pin as the standard pin of this conversion through after relatively.
As shown in Figure 5, the corresponding point step between described searching standard pin model and the sparse grid pin model is:
1) respectively to standard pin and sparse grid pin modelling right angle bounding box;
2) according to the ball of foot of sparse model, the pin stage casing, the part of body behind the pin, the Y-axis of bounding box to being that pin length is to being divided into three; The thumb of pin stick up a place sparse model carry out Z axially promptly height to being divided into two; The standard pin is carried out same processing; (see figure 3)
3), in the little bounding box of standard pin of correspondence, seek nearest point, as its corresponding point to each point in the little bounding box of sparse grid division.
As shown in Figure 6, describedly remodify the control vertex of sparse grid pin model, by new control vertex sparse grid pin model rebuild: can be divided into following several steps according to corresponding point:
(1) in the bounding box of having constructed, set up local coordinate system O '-STU, S wherein, T, the U direction respectively with former coordinate system X, Y, the Z direction is identical.If the coordinate of any 1 X in local coordinate system is among the Cartesian coordinates O-XYZ (u), being write as vector representation then has for s, t:
X=X 0+sS+tT+uU (1)
X 0Be the coordinate vector of initial point in Cartesian coordinates of local coordinate system.Can obtain by the vector operation theory in the linear algebra
S = T × U · ( X - X 0 ) T × U · S , t = S × U · ( X - X 0 ) S × U · T , u = S × T · ( X - X 0 ) S × T · U - - - ( 2 )
(2) structure control vertex grid P on bounding box I, j, k, respectively along S, three directions of T and U are with being parallel to O ' TU, O ' SU, and the equidistant cross section of O ' ST coordinate surface is with O ' S, and O ' T and O ' U are divided into l, and m and n is interval, then P I, j, kCan be expressed as:
P i , j , k = X 0 + i l S + j m T + k n U - - - ( 3 )
In the bounding box arbitrarily the Cartesian coordinate X of any can be expressed as:
X ( s , t , u ) = Σ i = 0 l Σ j = 0 m Σ k = 0 n P i , j , k B il ( s ) B jm ( t ) B kn ( u ) - - - ( 4 )
B wherein Il(S), B Jm(t), B Kn(u) be l respectively, m, n Bernstein polynomial basis function.
Above-mentioned l, m, n is according to the l=1 as can be known of the dividing method in the corresponding point step of seeking between standard pin model and the sparse grid pin model, m=3, n=2.
(3) find the solution the position of new control vertex.Because the three-dimensional feature pin model curved surface that generates still will satisfy (4) formula, the expression mode that is simplified to matrix so then can be write as following form:
Q '=B (P+ Δ P) or Δ Q=B Δ P (5)
Q represents the matrix that the vector of apex coordinate on the curved surface constitutes, and P is the coordinate vector matrix of control vertex, and B represents the weight function matrix that calculated by the Bernstein polynomial basis function.Δ Q can obtain by the right vectorial difference of corresponding point, and B is a known matrix, and our problem has just become the Δ P in the solving equation so, that is:
ΔP=B +ΔQ (6)
B wherein +The generalized inverse matrix of representing matrix B, it is not a square formation, because the number on the summit on the number of control vertex and the curved surface under normal circumstances is unequal.Below we work be exactly to calculate B +
(4) find the solution the generalized inverse of B.The generalized inverse that formula below we will adopt is calculated B
B +=C T(D TBC T) -1D T (7)
The first step will be carried out the full rank decomposition to B, promptly obtains B=DC, and wherein C is a upper triangular matrix, and D is following triangle battle array.C can obtain according to the row elementary transformation, and D preserves the accumulation of its contrary elementary transformation matrix simultaneously.Just can be easy to obtain B according to following formula then +.
(5) re-construct curved surface, generate three-dimensional feature pin model.Go on foot the B that obtains to (4) +Be updated in the equation in (3) step, just can obtain Δ P.Thereby obtain new gating matrix P ', just can obtain on the new curved surface according to formula (4) like this is the coordinate vector of the every bit on the net result three-dimensional feature pin model.
Our method is realized with VC++6.0 and OpenGL under windows XP environment.The test case that we adopt is as follows: counting that the standard pin model (seeing accompanying drawing 1) that test is chosen is comprised is 4661, and the point data of obtained sparse grid pin model is 166.It is 16 that the adaptability in tactics that the B spline base function produces is changed rank of matrix.
Fig. 1, Fig. 2 a and Fig. 2 b are the once results of test that seminar does, and are a width of cloth comparison diagrams that is transformed to three-dimensional feature pin model by the standard pin.
For reliability and the correctness of verifying this algorithm, we have adopted the experiment process as accompanying drawing 7 to carry out the method that three groups of independent experiments are averaged then respectively.And the measurement result of result and actual user's craft compared.
Input vision data measuring system is chosen the pin long vector after obtaining three-dimensional sparse grid pin model by hand, because the pin long vector is to the influence of the accuracy of pin shape parameter very big (pin long vector determined long and each pin type salient point of pin with respect to the y of heel point to coordinate position), so opening comes out manually to choose.Carry out three-dimensional feature pin Model Reconstruction after the pin long vector is chosen, and the pin shape parameter that obtains is used to estimate the authenticity of three-dimensional feature pin model.This part system can adopt algorithm described in the implementation process automatically, and obtains to remove and enclose each pin shape parameter of being outside one's consideration.Obtain reasonably degree of enclosing parameter by adjusting then to degree of enclosing cross section.Following table is our test result and the contrast table between the legitimate reading:
Test result and legitimate reading contrast table unit: millimeter (mm)
Parameter Test figure 1 Test figure 2 Test figure 3 The test average Actual measured value
Pin is long 245.048004 243.501999 245.113998 244.554667 244
Tuberosity of distal phalange 245.048004 243.501999 245.113998 244.554667 244
Pin thumb evagination point position 215.582993 217.279007 212.117996 214.993332 217
Pin little toe evagination point position 182.819000 181.447006 181.960007 182.075337 182
Pin first sole of the foot toe-end point position 169.039993 174.753998 170.988007 171.593999 170
Pin the 5th sole of the foot toe-end point position 148.860001 151.343994 147.970993 149.391662 149
Pin flank position 100.470001 99.835701 100.497002 100.267568 100
Pin center of gravity position 44.108700 43.830299 44.120499 44.019832 44
Pin is wide 94.000000 92.000000 94.000000 93.333333 93
The pin thumb is wide 43.000000 42.000000 43.000000 42.666667 43
The pin little toe is wide 45.000000 47.000000 46.000000 46.000000 46
Pin first sole of the foot toe bit wide 47.000000 46.000000 47.000000 46.666667 47
Pin the 5th sole of the foot toe bit wide 46.000000 46.000000 46.000000 46.000000 46
Pin flank position is wide 43.897999 42.964001 43.897999 43.586666 44
Pin center of gravity position is wide 63.638000 62.284000 63.638000 63.186667 64
The pin sole of the foot encloses 236.649994 230.220001 235.869995 234.246663 236
The pin instep encloses 245.600006 244.330002 238.770004 242.900004 244
The pin head is thick 24.000000 32.000000 32.000000 29.333333 30
The prominent point of heel is high 42.000000 48.000000 48.000000 46.000000 45
The error aspect, as can be seen from the above table, our test result and actual persons pin meet substantially.The shake that three test figures show derives from following several aspect: operating personnel are to the operation of system, the pin long vector choose the Acquisition Error of vision data, the error that the hand dipping real data produces.

Claims (5)

1. three-dimension foot type fast acquiring method based on standard pin distortion towards sparse grid is characterized in that the step of method is as follows:
1) sets up standard pin type storehouse;
2) set up three-dimensional coordinate, select the tiptoe heel point structure pin long vector of the sparse grid pin of input;
3) rotation sparse grid pin model makes step 2) in the pin long vector parallel with Y-axis;
4) from standard pin type storehouse, choose the standard pin model that the wide information of the long pin of pin is close with it according to the wide information of the long pin of pin;
5) adjust the posture and the position of sparse grid pin model, make it to be evenly distributed on standard pin model around;
6) corresponding point between searching standard pin model and the sparse grid pin model;
7) remodify the control vertex of sparse grid pin model according to corresponding point, by new control vertex sparse grid pin model is rebuild, thereby obtained three-dimension foot type data.
2. a kind of three-dimension foot type fast acquiring method based on the distortion of standard pin towards sparse grid as claimed in claim 1 is characterized in that the corresponding point step between described searching standard pin model and the sparse grid pin model is:
1) respectively to standard pin and sparse grid pin modelling right angle bounding box;
2) according to the ball of foot of sparse grid pin model, the pin stage casing, the part of body behind the pin, the Y-axis of bounding box to being that pin length is to being divided into three; The thumb of pin stick up a place sparse grid pin model carry out the Z axle promptly height to being divided into two; The standard pin is carried out same processing;
3), in the little bounding box of standard pin of correspondence, seek nearest point, as its corresponding point to each point in the little bounding box of sparse grid pin model division.
3. a kind of three-dimension foot type fast acquiring method based on the distortion of standard pin towards sparse grid as claimed in claim 1 is characterized in that the described control vertex that remodifies sparse grid pin model according to corresponding point; Adopt B-spline surface to represent to sparse grid pin model; Ask the relative vector between generalized inverse, sparse grid pin model and the standard pin model corresponding point of sparse grid basis function matrix poor, above-mentioned both multiply each other relative positions of obtaining new and old control vertex are poor; Old control vertex and the addition of relative position difference vector obtain new control vertex coordinate.
4. a kind of three-dimension foot type fast acquiring method as claimed in claim 1 based on the distortion of standard pin towards sparse grid, it is characterized in that, the described standard pin type storehouse of setting up: obtain three-dimension foot type in the pin type storehouse with the way of laser scanning, according to the age, sex, pin is long, and the people who chooses regular pin type respectively carries out laser scanning, set up the wide concordance list of the long pin of pin, form standard pin type storehouse.
5. a kind of three-dimension foot type fast acquiring method as claimed in claim 1 based on the distortion of standard pin towards sparse grid, it is characterized in that, describedly choose the standard pin model that the wide information of the long pin of pin is close with it according to the wide information of the long pin of pin from standard pin type storehouse: its step is
1) in standard pin type storehouse, travels through all standard pin type information, obtain the long and wide data of pin of pin of pin type in the storehouse;
2) after two parameters of the sparse grid pin model of and wide data of pin and input long with pin in the Current Standard pin type storehouse are done respectively and differed from, get the absolute value of the product of two differences;
3), select in the standard pin type storehouse of absolute value minimum the pin type as the standard pin of this conversion through after relatively.
CNB2005100612723A 2005-10-26 2005-10-26 Sparse grid-oriented three-dimensional foot type fast acquiring method based on standard foot deformation Expired - Fee Related CN100349189C (en)

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CN101872490A (en) * 2010-07-02 2010-10-27 北京理工大学 Improved three-dimensional model deformation method under abrupt change in coordinate axis direction
CN101894389A (en) * 2010-07-02 2010-11-24 北京理工大学 Rotation angle interpolation-based three-dimensional model torsional deformation method
CN106327570B (en) * 2016-08-16 2019-04-12 华中科技大学 A kind of customized insole model generating method and system based on foot's threedimensional model
CN107248192A (en) * 2017-06-27 2017-10-13 广州视源电子科技股份有限公司 Solid figure method for drafting, device, equipment and storage medium
CN111882659B (en) * 2020-07-21 2022-04-22 浙江大学 High-precision human body foot shape reconstruction method integrating human body foot shape rule and visual shell

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5175806A (en) * 1989-03-28 1992-12-29 Computer Design, Inc. Method and apparatus for fast surface detail application to an image
US6441816B1 (en) * 1999-12-29 2002-08-27 Intel Corporation Method for modeling and rendering complex surfaces using local height maps
CN1544883A (en) * 2003-11-25 2004-11-10 浙江大学 Three-dimensional foot type measuring and modeling method based on specific grid pattern
CN1607550A (en) * 2003-10-15 2005-04-20 财团法人工业技术研究院 Method for constructing three-dimensional regularized color model

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5175806A (en) * 1989-03-28 1992-12-29 Computer Design, Inc. Method and apparatus for fast surface detail application to an image
US6441816B1 (en) * 1999-12-29 2002-08-27 Intel Corporation Method for modeling and rendering complex surfaces using local height maps
CN1607550A (en) * 2003-10-15 2005-04-20 财团法人工业技术研究院 Method for constructing three-dimensional regularized color model
CN1544883A (en) * 2003-11-25 2004-11-10 浙江大学 Three-dimensional foot type measuring and modeling method based on specific grid pattern

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
网格模型的局部编辑算法 秦绪佳,等.计算机辅助设计与图形学学报,第16卷第4期 2004 *

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