CN107067462A - Fabric three-dimensional draping shape method for reconstructing based on video flowing - Google Patents

Abstract

The invention provides a method for reconstructing the three-dimensional drape shape of fabric based on video stream, which is characterized in that it comprises the following steps: cutting the fabric into a circle and placing it on the tray of the drape instrument, and then placing the top plate with a checkerboard installed on the fabric. The center is located between the tray and the top plate, and the center of the chessboard coincides with the center of the top plate; the camera equipment is moved at a constant speed along the upper circular track and the lower circular track to take pictures; the video stream captured in the second step is converted into an image sequence; respectively use SIFT The algorithm and the Harris algorithm perform feature point detection on the image sequence obtained in the third step; obtain a 3D point cloud; perform Poisson reconstruction on the 3D point cloud to obtain a reconstructed model, perform texture mapping, and obtain a 3D draping model. The method for reconstructing the three-dimensional draping form of the fabric based on the video stream provided by the present invention has a simple and stable reconstruction process, high precision of the reconstruction result, and can truly and completely reflect the three-dimensional draping form of the fabric.

Description

Fabric 3D Drape Shape Reconstruction Method Based on Video Stream

technical field

The invention relates to a method for obtaining a three-dimensional color model of fabric drape by collecting video of fabric drape.

Background technique

The drape shape of the fabric mainly refers to the three-dimensional appearance of the fabric drape surface. The existing technology mainly indirectly reflects the drape shape of the fabric through the two-dimensional information on the drape projection, which has relatively large limitations.

Contents of the invention

The purpose of the invention is to obtain the three-dimensional appearance of the fabric hanging curved surface through the video stream.

In order to achieve the above object, the technical solution of the present invention provides a method for reconstructing the three-dimensional drape shape of fabric based on video stream, which is characterized in that it comprises the following steps:

The first step is to cut the fabric into a circle and place it on the tray of the drape instrument, and then place the top plate with the chessboard installed. The fabric is centered between the tray and the top plate, and the center of the chessboard coincides with the center of the top plate;

The second step is to move the camera equipment at a constant speed along the upper circular track and the lower circular track to take pictures. The upper circular track is located directly above the fabric, and the lower circular track is flush with the hanging bottom edge of the fabric;

The third step is to convert the video stream captured in the second step into an image sequence;

The 4th step, utilize SIFT algorithm and Harris algorithm to carry out feature point detection to the image sequence that the 3rd step obtains respectively;

The fifth step, after extracting the feature points on all images in the image sequence, calculate the nearest neighbor matching between each image and the feature points in the image to be matched;

The sixth step is to find the external parameter matrix between two images in the image sequence, and normalize it to the same coordinate system to calculate the three-dimensional coordinates of the feature points, thereby obtaining a three-dimensional point cloud;

The seventh step is to perform Poisson reconstruction on the 3D point cloud obtained in the sixth step to obtain the reconstruction model;

In the eighth step, texture mapping is performed on the reconstructed model obtained in the seventh step to obtain a three-dimensional overhanging model.

Preferably, in the first step, if the fabric is a solid-color fabric, grid lines are drawn on the surface of the fabric.

Preferably, in the third step, corresponding screenshots are extracted from the video captured in the second step according to a preset sampling density, so as to convert the video stream into an image sequence.

Preferably, in the fourth step, using the SIFT algorithm to perform feature point detection on the image sequence includes the following steps:

Step 4A.1. For any two-dimensional image I(x, y) in the image sequence, the convolution of the two-dimensional image I(x, y) with the Gaussian kernel function G(x, y, σ) is obtained in Scale-space image L(x, y, σ) at different scales, σ is the width parameter of the function, which controls the radial range of the function. Establish the DOG pyramid of the image, D(x, y, σ) is the difference between two adjacent scale images, then:

D(x,y,σ)=(G(x,y,kσ)-G(x,y,σ))*I(x,y)=L(x,y,kσ)-L(x,y , σ), where k is the scale parameter;

Step 4A.2, specify the direction parameter for each feature point, so that the operator has rotation invariance:

The gradient modulus at the pixel point (x, y) is m(x, y), then:

The gradient direction at the pixel point (x, y) is θ(x, y), then:

θ(x,y)=αtan2((L(x,y+1,σ)-L(x,y-1,σ))/(L(x+1,y,σ)-L(x-1 , y, σ))), where α is the eigenvalue of the principal curvature;

Step 4A.3. Generation of feature point descriptors: Rotate the coordinate axis to the direction of feature points, and describe each key point using a total of 16 seed points of 4×4, so that 128 data are generated for one feature point, namely 128-dimensional SIFT feature vector.

Preferably, in the fourth step, utilizing the Harris algorithm to perform feature point detection on the image sequence includes the following steps:

Step 4B.1. The small window centered on the pixel point (x, y) moves u in the X direction and moves v in the Y direction. The analytical formula for the grayscale change is:

In the formula, E(x, y) is the amount of gray scale change, o is an infinitesimal operator;

Step 4B.2, transform E(x, y) into quadratic form:

In the formula, M is a real symmetric matrix, I _x is the gradient of the image I (x, y) in the X direction, and I _y is the gradient of the image I (x, y) in the Y direction;

Step 4B.3, define the corner response function CRF as:

CRF=det (M)-0.04*trace ² (M), in the formula, det (M) is the determinant of real symmetric matrix M, and trace (M) is the track of real symmetric matrix M;

The point where the local maximum value of the corner response function CRF is located is the corner point.

Preferably, in the sixth step, the relationship between the two-dimensional point p=[u ₀ , v ₀ ] ^T and its corresponding three-dimensional point P _w =[x, y, z] ^T is:

p=K[R/t]P _w , in the formula, [R/t] is the external parameter matrix of the camera equipment, indicating the position of the camera equipment in the world coordinate system, and K is the internal parameter matrix of the camera equipment, which is the fixed parameter of the lens , the relationship between the same point P _w on the object corresponding to points p ₁ and p ₂ in any two images is:

p ₁ Fp ₂ ＝0, where F is the fundamental matrix, F＝K ^-T [t]×RK ^-1 .

Preferably, in the sixth step, the obtained three-dimensional coordinates are further optimized using the BA algorithm, thereby obtaining the three-dimensional point cloud, expressed as follows:

In the formula, is the two-dimensional coordinates of the jth feature point in the i-th image, K[R _i |t _i ]X ^j is the point Reprojected coordinates corresponding to 3D points.

Preferably, the seventh step includes the following steps:

Step 7.1, establish the octree topological relationship for the 3D point cloud data obtained in the sixth step, and add the scattered 3D point cloud data into the octree;

Step 7.2. Set the spatial function F _c for each node in the octree topology:

In the formula, R _c is the center of the node, r _w is the width of the node, is the basis function, R is any data point, and the coordinates of any point in the 3D point cloud are set as (x, y, z), then the function space F(x, y, z) is expressed as:

F(x, y, z)=(A(x)A(y)A(z)) ³ , where, is the filter function, let the filter function The variable is t, then there are:

Step 7.3, in the case of uniform sampling, assuming that the divided blocks are constant, through the vector field Approximate the gradient of the indicator function, define the approximation to the gradient field of the indicator function as Then there are:

In the formula, s is a point in the point cloud, S is the set of point cloud samples, o is a node in the octree, Ngbr _D (s) is the eight nodes with depth D of the nearest neighbor sp, and sp is the point cloud sample , α _{o, s} is the weight of cubic linear interpolation, F _o (q) is the node function, is the normal of the point cloud sample;

Step 7.4, obtain the vector field according to the equation shown in step 7.3 Finally, the Poisson equation is obtained by using the Laplacian matrix iteration method solution, where Δ represents increment, is the position estimate of the sampling point, is a vector differential operator.

Step 7.5, through the position estimation of the sampling point Use its mean for isosurface extraction:

In the formula, is the isosurface, q is the point cloud data, is the point cloud sample distribution function, r is the mean value of the point cloud sample distribution coordinates, Distribution coordinates for point cloud samples;

In step 7.6, splice the isosurfaces extracted in step 7.5 to obtain the reconstructed model.

Preferably, in the eighth step:

Assuming that the acquired texture image sequence is I= _{ I ₁ , I ₂ , I ₃ ,...,In }, when each image is acquired, the projection matrix set of the camera device relative to the object is P={P ₁ , P ₂ , P ₃ ,..., P _n }, then the texture mapping function (u, v) is defined as:

(u,v)=F(x,y,z,I,P)

To back-project each 3D point into the corresponding 2D image, there are:

y=P _i Y, where, y=(x, y) ^T is the corresponding point projected back on the two-dimensional image, Y=(x, y, z) ^T is a three-dimensional point in the three-dimensional point cloud, P _i is the projection matrix of the viewpoint where the image is located.

Preferably, after the eighth step, it also includes:

The ninth step is to convert the three-dimensional overhang model from the X _C Y _C Z _C coordinate system to the X _D Y _D Z _D coordinate system. The X _C Y _C Z _C coordinate system is the coordinate system with the center of the top plate of the overhang model as the origin. X The _D Y _D Z _D coordinate system is the coordinate system with the point selected by the user as the origin.

The method for reconstructing the three-dimensional draping form of the fabric based on the video stream provided by the present invention has a simple and stable reconstruction process, high precision of the reconstruction result, and can truly and completely reflect the three-dimensional draping form of the fabric.

Description of drawings

Fig. 1 is the schematic diagram of the drape instrument used in the present invention;

Fig. 2 is the schematic diagram of chessboard;

Fig. 3 is a schematic diagram of video acquisition of the present invention;

Figure 4 is a joint feature point detection map of SIFT and Harris;

Figure 5 is a pinhole camera model;

Fig. 6 is a schematic diagram of coordinate transformation.

detailed description

Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

The invention relates to a method for reconstructing a three-dimensional fabric drape form based on a video stream, comprising the following steps:

The first step is to cut the fabric into a circle and place it on the tray 2 of the drape instrument 1 as shown in Figure 1, then place the top plate with the checkerboard 3 as shown in Figure 2, and the fabric is centered between the tray 2 and the top plate Between, and the center of chessboard 3 coincides with the center of the top board.

Both the diameter of the tray 2 and the top plate are 12 cm, and the surface of the top plate adopts patterns of color and texture. The size of the chessboard 3 is 6cm×4cm, and the side length L of the grid is 1cm.

The fabric was cut into circular samples with a diameter of 24 cm. For solid-color fabrics, it is necessary to use a marker pen of a different color from the fabric to draw grid lines with a spacing of 3cm on the surface of the fabric.

The second step, as shown in Figure 3, is to move the camera at a constant speed along the upper and lower circular tracks to take pictures. The upper circular track is located directly above the fabric, and the lower circular track is flush with the hanging bottom edge of the fabric. About 5 seconds.

In the third step, corresponding screenshots are extracted from the captured video at a sampling density of 4 frames per second, so as to convert the video stream captured in the second step into an image sequence.

In the fourth step, the feature point detection is performed on the image sequence obtained in the third step by using the SIFT algorithm and the Harris algorithm respectively.

Using the SIFT algorithm to perform feature point detection on image sequences includes the following steps:

Step 4A.1. For any two-dimensional image I(x, y) in the image sequence, the convolution of the two-dimensional image I(x, y) with the Gaussian kernel function G(x, y, σ) is obtained in Scale space image L(x, y, σ) at different scales, σ is the width parameter of the function, which controls the radial range of the function, and establishes the DOG (Difference of Gaussian) pyramid of the image, D(x, y, σ ) is the difference between two adjacent scale images, then:

D(x,y,σ)=(G(x,y,kσ)-G(x,y,σ))*I(x,y)=L(x,y,kσ)-L(x,y , σ), where k is the scale parameter;

Step 4A.2, specify the direction parameter for each feature point, so that the operator has rotation invariance:

The gradient modulus at the pixel point (x, y) is m(x, y), then:

The gradient direction at the pixel point (x, y) is θ(x, y), then:

θ(x,y)=αtan2((L(x,y+1,σ)-L(x,y-1,σ))/(L(x+1,y,σ)-L(x-1 , y, σ))), where α is the eigenvalue of the principal curvature;

Step 4A.3. Generation of feature point descriptors: Rotate the coordinate axis to the direction of feature points, and describe each key point using a total of 16 seed points of 4×4, so that 128 data are generated for one feature point, namely 128-dimensional SIFT feature vector.

Using the Harris algorithm to perform feature point detection on image sequences includes the following steps:

Step 4B.1. The small window centered on the pixel point (x, y) moves u in the X direction and moves v in the Y direction. The analytical formula for the grayscale change is:

In the formula, E(x, y) is the amount of gray scale change, o is an infinitesimal operator;

Step 4B.2, transform E(x, y) into quadratic form:

In the formula, M is a real symmetric matrix, I _x is the gradient of the image I (x, y) in the X direction, and I _y is the gradient of the image I (x, y) in the Y direction;

Step 4B.3, define the corner response function CRF as:

CRF=det (M)-0.04*trace ² (M), in the formula, det (M) is the determinant of real symmetric matrix M, and trace (M) is the track of real symmetric matrix M;

The point where the local maximum value of the corner response function CRF is located is the corner point.

As shown in Figure 4, the star points are SIFT feature points, and the circle points are Harris feature points.

Step 5: After extracting the feature points on all the images in the image sequence, calculate the nearest neighbor matching between each image and the feature points in the image to be matched.

Calculating the distance U _ab between two eigenvectors a={a ₁ , a ₂ , . . . , a _n }, b={b ₁ , b ₂ , . . . , b _n } is expressed as follows:

The sixth step is to calculate the external parameter matrix between two images in the image sequence, and normalize it to the same coordinate system to calculate the three-dimensional coordinates of the feature points, thereby obtaining a three-dimensional point cloud.

As shown in Figure 5, according to the pinhole camera model, the relationship between the two-dimensional point p = [u ₀ , v ₀ ] ^T on the photo and its corresponding three-dimensional point P _w = [x, y, z] ^T is:

p=K[R/t]P _w , where [R/t] is the external parameter matrix of the camera, indicating the position of the camera in the world coordinate system, and K is the internal parameter matrix of the camera, which is the fixed parameter of the lens. The relationship between the corresponding points p ₁ and p ₂ in any two images of the same point P _w is:

p ₁ Fp ₂ =0, where F is the fundamental matrix, F=K ^-T [t]×RK ^-1 .

The obtained three-dimensional coordinates are further optimized using the BA (Bundle adjustment) algorithm, thereby obtaining the three-dimensional point cloud, which is expressed as follows:

In the formula, is the two-dimensional coordinates of the jth feature point in the i-th image, K[R _i |t _i ]X ^j is the point Reprojected coordinates corresponding to 3D points.

The seventh step is to perform Poisson reconstruction on the 3D point cloud obtained in the sixth step to obtain the reconstruction model, including the following steps:

Step 7.1, establish the octree topological relationship for the 3D point cloud data obtained in the sixth step, and add the scattered 3D point cloud data into the octree;

Step 7.2. Set the spatial function F _c for each node in the octree topology:

In the formula, R _c is the center of the node, r _w is the width of the node, is the basis function, R is any data point, and the coordinates of any point in the 3D point cloud are set as (x, y, z), then the function space F(x, y, z) is expressed as:

F(x, y, z)=(A(x)A(y)A(z)) ³ , where, is the filter function, let the filter function The variable is t, then there are:

Step 7.3, in the case of uniform sampling, assuming that the divided blocks are constant, through the vector field Approximate the gradient of the indicator function, define the approximation to the gradient field of the indicator function as Then there are:

In the formula, s is the point in the point cloud, S is the set of point cloud samples, o is the node in the octree, Ngbr _D (s) is the eight nodes with depth D of the nearest neighbor sp, and sp is the point cloud sample , α _{o, s} is the weight of cubic linear interpolation, F _o (q) is the node function, is the normal of the point cloud sample;

Step 7.4, obtain the vector field according to the equation shown in step 7.3 Finally, the Poisson equation is obtained by using the Laplacian matrix iteration method The solution of , where Δ is the expression increment, is the position estimate of the sampling point, is a vector differential operator;

Step 7.5, through the position estimation of the sampling point Use its mean for isosurface extraction:

In the formula, is the isosurface, q is the point cloud data, is the point cloud sample distribution function, r is the mean value of the point cloud sample distribution coordinates, is the point cloud sample distribution function;

In step 7.6, splice the isosurfaces extracted in step 7.5 to obtain the reconstructed model.

In the eighth step, texture mapping is performed on the reconstructed model obtained in the seventh step to obtain a three-dimensional overhanging model. In this step:

Assuming that the obtained texture image sequence is I={I ₁ , I ₂ , I ₃ ,...,I _n }, when acquiring each image, the projection matrix set of the camera relative to the object is P={P ₁ , P ₂ , P ₃ ,..., P _n }, then the texture mapping function (u, v) is defined as:

(u,v)=F(x,y,z,I,P)

To back-project each 3D point into the corresponding 2D image, there are:

y=P _i Y, where, y=(x, y) ^T is the corresponding point projected back on the two-dimensional image, Y=(x, y, z) ^T is a three-dimensional point in the three-dimensional point cloud, P _i is the projection matrix of the viewpoint where the image is located.

The ninth step is to convert the three-dimensional overhang model from the X _C Y _C Z _C coordinate system to the X _D Y _D Z _D coordinate system. The X _C Y _C Z _C coordinate system is the coordinate system with the center of the top plate of the overhang model as the origin. X The _D Y _D Z _D coordinate system is the coordinate system with the point selected by the user as the origin.

In conjunction with Figure 6, the ninth step includes the following steps:

Step 9.1, index the corresponding coordinates in the three-dimensional point cloud model according to the coordinates of the checkerboard corner points in the image;

Step 9.2, calculate the top plate plane normal vector according to the three-dimensional corner point coordinates obtained in step 9.1

Step 9.3, translate the O ₁ point to the coordinate origin to obtain the transformation matrix T ₁ ;

Step 9.4, rotate O ₁ P ₁ clockwise around the Y _D axis by θ _y , coincide with the Y _D O ₀ Z _D plane, and obtain the transformation matrix T ₂ ;

Step 9.5, rotate O ₁ P ₁ clockwise around the X _D axis by θ _x to coincide with the Z _D axis to obtain the transformation matrix T ₃ ;

Step 9.6, transformation matrix T from the calibration coordinate system to the suspension coordinate system = T ₁ ×T ₂ ×T ₃ ;

Step 9.7: Multiply the point coordinates on the three-dimensional overhang model by 1/l, where l is the distance between adjacent three-dimensional corner points.

Claims (10)

1. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing, it is characterised in that comprise the following steps：

The first step, by fabric cut out it is rounded after be placed on the pallet of Pendant meter (1) (2), be subsequently placed with that chessboard (3) is installed Take over a business, between fabric is centrally placed in pallet (2) and taken over a business, and chessboard (3) center and take over a business center superposition；

Second step, imaged along upper annular trace, lower annular trace at the uniform velocity dollying equipment, upper annular trace is located at fabric Surface, lower annular trace is concordant with the pendency base of fabric；

3rd step, the video flowing that second step is photographed is converted into image sequence；

4th step, it is utilized respectively the image sequence that SIFT algorithms and Harris algorithms obtain to the 3rd step and carries out feature point detection；

5th step, extract after characteristic point in image sequence on all images, calculate per piece image with image to be matched Characteristic point arest neighbors matching；

6th step, outer ginseng matrix between image two-by-two is obtained in image sequence, and be normalized under the same coordinate system The three-dimensional coordinate of characteristic point is calculated, so as to obtain three-dimensional point cloud；

7th step, the three-dimensional point cloud progress Poisson reconstruction obtained to the 6th step, obtain reconstruction model；

8th step, on the reconstruction model that the 7th step is obtained carry out texture mapping, obtain three-dimensional drape model.

2. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In the first step, if fabric is pure color fabric, grid lines is being drawn in fabric face.

3. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In 3rd step, the video photographed to second step extracts corresponding sectional drawing by sampling density set in advance, so as to will regard Frequency circulation turns to image sequence.

4. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In 4th step, feature point detection is carried out to image sequence using SIFT algorithms and comprised the following steps：

Step 4A.1, for any one secondary two-dimensional image I (x, y) in image sequence, by two-dimensional image I (x, y) and Gaussian kernel Function G (x, y, σ) convolution obtains the scale space images L (x, y, σ) under different scale, and σ is the width parameter of function, The radial effect scope of function is controlled, the DOG pyramids of image are set up, D (x, y, σ) is the difference of two adjacent scalogram pictures, Then have：

In D (x, y, σ)=(G (x, y, k σ)-G (x, y, σ)) * I (x, y)=L (x, y, k σ)-L (x, y, σ), formula, k joins for yardstick Number；

Step 4A.2, be each characteristic point assigned direction parameter, operator is possessed rotational invariance：

The gradient modulus value at pixel (x, y) place is m (x, y), then has：

The gradient direction at pixel (x, y) place is θ (x, y), then has：

In θ (x, y)=α tan2 ((L (x, y+1, σ)-L (x, y-1, σ))/(L (x+1, y, σ)-L (x-1, y, σ))), formula, based on α The characteristic value of curvature；

Step 4A.3, feature point description generation：Reference axis is rotated into characteristic point direction, 4 × 4 are used to each key point Totally 16 seed points are described, and so just produce 128 data, i.e., the SIFT features vector of 128 dimensions for a characteristic point.

5. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 4, it is characterised in that In 4th step, feature point detection is carried out to image sequence using Harris algorithms and comprised the following steps：

Step 4B.1, the wicket centered on pixel (x, y) move u in the X direction, and v is moved in the Y direction, its gray scale The analytic expression of change is：

In formula, E (x, y) is grey scale change amount,O is dimensionless operator；

Step 4B.2, E (x, y) is turned into quadratic form had：

In formula, M is real symmetric matrix,I_xIt is image I (x, y) in X side To gradient, I_yFor the gradients of image I (x, y) in the Y direction；

Step 4B.3, angle point receptance function CRF is defined as：

CRF=det (M) -0.04*trace²(M), in formula, det (M) is real symmetric matrix M determinant, and trace (M) is real right Claim matrix M mark；

Angle point receptance function CRF local maximum point is angle point.

6. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In 6th step, two-dimensional points p=[u₀, v₀]^TCorresponding three-dimensional point P_w=[x, y, z]^TBetween relation be：

P=K [R/t] P_w, in formula, [R/t] is the outer ginseng matrix of picture pick-up device, represents position of the picture pick-up device in world coordinate system Put, K is the internal reference matrix of picture pick-up device, be the point of identical one P on camera lens preset parameter, object_wIt is right in any two images Should point p₁And p₂Relation be：

p₁Fp₂=0, in formula, matrix based on F, F=K^-T[t]×RK^-1。

7. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In 6th step, three-dimensional coordinate will be obtained and further optimized using BA algorithms, so as to obtain the three-dimensional point cloud, represented such as Under：

In formula,It is the two-dimensional coordinate of j-th of characteristic point in i-th image, K [R_i| t_i]X^jIt is a littleThe re-projection coordinate of corresponding three-dimensional points.

8. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that institute The 7th step is stated to comprise the following steps：

Step 7.1, Octree topological relation is set up to the three dimensional point cloud that the 6th step is obtained, by three dimensional point cloud at random All it is added in Octree；

Step 7.2, for each node installation space function F in Octree topological relation_c：

In formula, R_cFor the center of node, r_wFor the width of node,For basic function, R is Arbitrary Digit Strong point, (x, y, z) is set to by the coordinate of any point in three-dimensional point cloud, then function space F (x, y, z) is expressed as：

F (x, y, z)=(A (x) A (y) A (z))³, in formula,For filter function, if filter functionVariable be t, then Have：

Step 7.3, in the case of uniform sampling, it is assumed that divide block is constant, pass through vector fieldApproach the ladder of indicator function Spend, definition is to the approximation of the gradient fields of indicator functionThen have：

In formula, s is the point in point cloud, and S is point cloud sample set, and o is in Octree Node, Ngbr_D(s) node that eight depth for being arest neighbors s.p are D, s.p is point cloud sample, α_{O, s}For trilinear interpolation Power, F_o(q) it is node function,For a normal for cloud sample；

Step 7.4, the equation according to step 7.3 obtain vector fieldAfterwards, pool is sought by the way of Laplacian Matrix iteration Loose measure journeySolution, in formula, Δ is representsIncrement,For the location estimation of sampled point,Calculated for vector differential Symbol；

Step 7.5, the location estimation by sampled pointIsosurface extraction is carried out with its average value：

In formula,For contour surface, q is cloud data,For a cloud sample distribution function, R is the average of point cloud sample distribution coordinate, For a cloud sample distribution function；

Step 7.6, the contour surface that step 7.5 is extracted are spliced, you can obtain reconstruction model.

9. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that In 8th step：

If the texture image sequence obtained is I={ I₁, I₂, I₃..., I_n, when obtaining each image, picture pick-up device is relative to thing The projection matrix collection of body is combined into P={ P₁, P₂, P₃..., P_n, then texture mapping function (u, v) is defined as：

(u, v)=F (x, y, z, I, P)

Each three-dimensional point back projection is returned in corresponding two dimensional image, had：

Y=P_iIn Y, formula, y=(x, y)^TIt is the corresponding points being projected back in on two dimensional image, Y=(x, y, z)^TIn being three-dimensional point cloud One three-dimensional point, P_iIt is the projection matrix of viewpoint residing for the image.

10. a kind of fabric three-dimensional draping shape method for reconstructing based on video flowing as claimed in claim 1, it is characterised in that After the 8th step, in addition to：

9th step, by three-dimensional drape model from X_CY_CZ_CCoordinate system is transformed into X_DY_DZ_DCoordinate system, X_CY_CZ_CCoordinate system is with the mould that dangles Type takes over a business the coordinate system that the center of circle is origin, X_DY_DZ_DCoordinate system is the coordinate system using user's Chosen Point as origin.