CN106331663A - System and method for acquiring interactive materials for portable devices - Google Patents

Abstract

The technical scheme of the invention comprises an interactive texture obtaining system and method for a portable device. The method comprises the steps of acquiring an image of flash texture and an image of non-flash texture; carrying out separation according to a separation parameter input by a user; creating a tangent vector based on a pixel difference of new light and dark images and calculating an initial value of a normal image; calculating the initial value of a diffuse reflection coefficient image; calculating the initial value of a highlight index image based on a highlight coefficient input by the user; calculating the initial value of a mirror image reflection coefficient image, and acquiring the final image of non-flash texture according to a highlight separation module, a normal image module, a diffuse reflection module and a highlight index module; and calculating a texture mapping parameter by using a nonlinear optimization technology, thereby generating a texture map. The method has the beneficial effects that the image of flash texture and the image of non-flash texture are collected by use of the portable devices such as a mobile phone and a digital single lens reflex, the more real texture map is generated interactively, and the existing problems that the texture production cycle is long, the cost is high and the precision is low in the fields of game production, television animation and the like are solved.

Description

System and method for acquiring interactive materials for portable devices

technical field

The invention relates to a system and method for acquiring interactive materials of a portable device, belonging to the field of graphics and image processing.

Background technique

In recent years, some progress has been made in using a single image to obtain material appearance properties and generate material maps. For example, technicians in the industry use CrazyBump to calculate Normal maps; in the field of research, Microsoft Research Asia has developed an interactive Material acquisition system AppGen. However, related systems all assume that the input single image has no obvious highlights, which limits the application of such methods. On the other hand, it is difficult to produce rendering effects similar to real lighting conditions with the material maps restored by this method.

YUV is a color coding method adopted by the European television system, and is a color space adopted by PAL and SECAM analog color television systems. In modern color TV systems, three-tube color cameras or color CCD cameras are usually used to capture images, and then the obtained color image signals are separated, enlarged and corrected to obtain RGB, and then the brightness signal Y and two signals are obtained through a matrix conversion circuit. A color difference signal BY (ie U), RY (ie V), and finally the sending end encodes the three signals of brightness and color difference respectively, and sends them out on the same channel. This color representation method is the so-called YUV color space representation. YUV's luminance signal Y and chrominance signals U, V are separated. If there is only the Y signal component but no U and V components, then the image represented in this way is a black and white grayscale image. Color TV uses YUV space just to solve the compatibility problem between color TV and black and white TV with brightness signal Y, so that black and white TV can also receive color TV signal.

Contents of the invention

Aiming at the deficiencies of the prior art, the technical solution of the present invention discloses an interactive material acquisition system method for portable devices, and the image data is obtained by taking photos with flash and without flash by mobile phones, SLR cameras and other devices.

The technical solution of the present invention includes an interactive material acquisition system for a portable device, which is characterized in that the system includes: an acquisition module, which is used to use a portable device to collect an image of a non-flash material, and is also used to use a portable device to collect an image of a material with a flash; The highlight separation module is used to convert the flash-free original RGB color space image collected by the acquisition module into a YUV color space image, and calculate a new shading image based on the separation parameters input by the user and the original shading image of the YUV color space image, Further separate the YUV image to obtain the mirror reflection map and the diffuse reflection map; the normal map module obtains the pixel difference of the new light and dark map according to the highlight separation module, and creates the first tangent vector and the second tangent vector in the tangent space. Further, according to the first The tangent vector and the second tangent vector calculate the initial value of the normal map; the diffuse reflection module is used to obtain the diffuse reflection map obtained by the specular separation, and calculate the initial value of the diffuse albedo map; the specular index module is used to obtain the specular separation The module obtains a new shading map, further, calculates the initial value of the specular index map based on the specular coefficient input by the user; the mirror reflection module is used to obtain the mirror reflection map obtained by the highlight separation, and further calculates the initial value of the mirror reflection coefficient map; parameters The optimization module is used to obtain the initialization parameters obtained according to the normal map module, the diffuse reflection module, the specular index module and the mirror reflection module, and also obtains the flash material image of the collection module, and calculates the texture map using nonlinear optimization technology parameters and generate material maps.

According to the interactive material acquisition system of the portable device, the highlight separation module also includes: a conversion sub-module, which is used to convert the original RGB color space of the collected flash-free material image into a material image in YUV color space, the original light and shade Figure Y1, as well as the original color map U and saturation V; the separation sub-module is used to set the separation parameter p according to user-defined settings, wherein the new light and dark map Y2 is Y2=Y1*p, further, based on the new light and dark map Y2, the original The color map U and saturation V are combined into a new RGB image, and the new RGB image is used as a diffuse reflection map, and the sum of the mirror reflection image and the diffuse reflection map is the original RGB image.

According to the interactive material acquisition system for portable devices, the normal map module further includes: for obtaining the pixel difference of the new shading map Y2, further, for any pixel point (i, j) the value D( i,j), the tangent vectors in the S and T directions are S(i,j)=(1,0,D(i+1,j)-D(i-1,j),), T(i ,j)=(0,1,D(i,j+1)-D(i,j-1),), then the product of the first tangent vector S and the second tangent vector T is the initial value of the normal map.

According to the interactive material acquisition system of the portable device, the diffuse reflection module further includes: for calculating the initial value of the diffuse reflection, wherein, the diffuse reflection coefficient map=diffuse reflection map/NdotL, NdotL is the pixel normal vector and Inner product of lighting vectors.

According to the interactive material acquisition system of the portable device, the specular index module further includes: calculating the initial value of the specular index map according to the specular parameter input by the user, and the calculation method is: the specular index map = new shading map Y2 * specular parameters.

According to the interactive material acquisition system of the portable device, the mirror reflection module also includes: an initial value for calculating the specular reflection coefficient map, and the calculation method is: specular reflection coefficient map=specular reflection map/pow(NdotH, g(x))/NdotL, where H represents the intermediate component of illumination and line of sight, NdotH is the inner product of the pixel normal vector and H, and g(x) represents the highlight index of any pixel point x.

According to the interactive material acquisition system of the portable device, the parameter optimization module also includes: a clustering sub-module, which is used to perform N clustering on all pixels of the flash-free image whose parameters are initialized, and N represents the number of clusters The acquisition parameter submodule is used to obtain the FOV parameters of the portable device, and initializes the illumination Lj and line of sight Vj parameters of each pixel of the flash image; the pixel processing submodule is used to classify each pixel of the flash image with Ci to process.

According to the interactive material acquisition system of the portable device, in a preferred embodiment, the pixel processing sub-module processes each pixel category Ci, including: a pixel selection sub-module, used to obtain the current pixel category Ci The value of any pixel point P; read the initial parameter submodule, which is used to obtain the initialization parameters of the pixel point of the non-flicker material image corresponding to the pixel point P; read the collection parameter submodule, which is used to read the value of the current pixel category Ci The RGB value of each pixel point Pj, the illumination Lj, and the line of sight Vj; the nonlinear optimization submodule is used to respectively call the pixel selection submodule, read the initial parameter submodule, and read the data obtained by the acquisition parameter submodule, Use nonlinear optimization to calculate the parameters of the pixel point P, and store it as the texture parameter base corresponding to the current pixel category Ci; the texture parameter sub-module is used to calculate the ratio of the remaining pixels of the current pixel category Ci to the pixel point P one by one, as The weight coefficients of the remaining pixels, and further, according to the texture parameter base of the current pixel category Ci, the texture parameters of the remaining pixels are calculated.

The technical solution of the present invention also includes a method for acquiring interactive materials, which is characterized in that the method includes: using a portable device to collect an image of a material without flash, and also using a portable device to collect an image of a material with flash; The original RGB color space image without flash is converted into a YUV color space image, and a new light and shade map is calculated based on the separation parameters input by the user and the original light and shade map of the YUV color space image, and the YUV image is further separated to obtain a mirror reflection map and a diffuse reflection map ; Obtain the pixel difference of the new light and dark image according to the highlight separation module, create the first tangent vector and the second tangent vector in the tangent space, and further calculate the initial value of the normal map according to the first tangent vector and the second tangent vector; obtain the highlight Separating the obtained diffuse reflectance map, calculating the initial value of the diffuse reflectance map; obtaining the highlight separation module to obtain a new light and shade map, and further calculating the initial value of the specular index map based on the specular coefficient input by the user; obtaining the mirror image obtained by the specular separation reflection map, and further calculate the initial value of the mirror reflection coefficient map; obtain the initialization parameters obtained according to the normal map module, the diffuse reflection module, the specular index module and the mirror reflection module, and also obtain the flash material image of the acquisition module, Computes material map parameters and generates material maps using non-linear optimization techniques.

Further, the method also includes: converting the original RGB color space of the collected non-glitter material image into a material image in YUV color space, the original light and shade map Y1, and the original color map U and saturation V; separation according to user-defined settings Parameter p, where the new shading image Y2 is Y2=Y1*p, further, based on the new shading image Y2, the original color map U and the saturation V are combined into a new RGB image, and the new RGB image is used as a diffuse reflection image, mirroring the reflection image The sum with the diffuse reflectance map is the original RGB image.

Further, the method further includes: obtaining the pixel difference of the new light and dark image Y2, further, for the value D(i,j) of any pixel point (i,j), the tangent vectors in the S and T directions are respectively S( i,j)=(1,0,D(i+1,j)-D(i-1,j),), T(i,j)=(0,1,D(i,j+1) -D(i,j-1),), then the product of the first tangent vector S and the second tangent vector T is the initial value of the normal graph.

Further, the method further includes: calculating the initial value of the diffuse reflection, wherein, the diffuse reflection coefficient map=diffuse reflection map/NdotL, and NdotL is the inner product of the pixel normal vector and the illumination vector.

Further, the method further includes: calculating the initial value of the highlight index map according to the highlight parameter input by the user, and the calculation method is: highlight index map=new shading map Y2*high light parameter.

Further, the method also includes: calculating the initial value of the specular reflection coefficient map, and the calculation method is: specular reflection coefficient map=specular reflection map/pow(NdotH,g(x))/NdotL, wherein H represents the middle component of illumination and line of sight , NdotH is the inner product of the pixel normal vector and H, and g(x) represents the highlight index of any pixel point x.

Further, the method also includes: performing N clustering on all pixels of the parameter-initialized non-flash image, where N represents the number of clusters; obtaining the FOV parameters of the portable device, and initializing the illumination Lj and line of sight of each pixel of the flash image. Vj parameter; process each pixel category Ci of the image with flash.

Further, the method also calculates each Ci, including: obtaining the value of any pixel point P in the current pixel category; obtaining the initialization parameters of the pixel point of the non-flicker material image corresponding to the pixel point P; reading the current pixel category The RGB value, illumination Lj, and line of sight Vj of each pixel point Pj of Ci; call the pixel selection sub-module, read the initial parameter sub-module, and read the data obtained by the acquisition parameter sub-module respectively, and use nonlinear optimization to calculate the pixel The parameters of the point P, and stored as the texture parameter base corresponding to the current pixel category Ci; calculate the ratio of the remaining pixels of the current pixel category Ci to the pixel P, as the weight coefficient of the remaining pixels, further, according to the current pixel category The texture parameter base of Ci, which calculates the texture parameters of the remaining pixels. Further, according to the texture parameter base of the current pixel category Ci, the texture parameters of the remaining pixels are calculated.

The beneficial effects of the present invention are: the material appearance acquisition system of the present invention uses mobile phones, SLR cameras and other portable devices to collect two images with flash and without flash, and interactively generates more realistic texture maps, thereby solving the problem of current game production, film and television animation In other fields, the material production cycle is long, the cost is high, and the precision is low.

Description of drawings

Fig. 1 shows a system block diagram according to an embodiment of the present invention;

Fig. 2 shows a detailed flowchart according to an embodiment of the present invention.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The interactive material acquisition system and method for portable devices of the present invention are suitable for graphics and image processing.

Fig. 1 shows a system block diagram and a flow chart according to an embodiment of the present invention. Among them, the acquisition module is used to realize the image source data acquisition, and the specular separation module, normal map module, diffuse reflection module, and mirror reflection module are used to initialize the material texture parameters according to the non-flash image, and the parameter module is used to adjust the flash material Image Processing. The acquisition module is used to collect the flash material image of the first portable device, and is also used to collect the non-flash material image of the second portable device; The flash original RGB color space image is converted into a YUV color space image, and the converted YUV image is converted into a new RGB image twice and separated to obtain a mirror reflection map and a diffuse reflection map, where the YUV color space includes the original light and shade map, the original color map and saturation; the normal map module is used to obtain a new shade map of the YUV color space image, create the first tangent vector and the second tangent vector in the tangent space according to the pixel difference of the new shading map, and further, according to the first tangent vector and The second tangent vector calculates the initial value of the normal map; the diffuse reflection module is used to obtain the diffuse reflection map obtained by the highlight separation, and calculates the initial value of the diffuse albedo map; the highlight index module is used to obtain the new highlight separation module. For the shade map, further, calculate the initial value of the specular index map based on the specular coefficient input by the user; the mirror reflection module is used to obtain the mirror reflection map obtained by the highlight separation, and further calculate the initial value of the mirror reflection coefficient map, according to the highlight The separation module, the normal map module, the diffuse reflection module and the specular index module obtain the final non-glitter material image; the parameter module is used to obtain the final non-glitter material image according to the specular separation module, the normal map module, the diffuse reflection module and the specular index module The initialization parameters of the material image are also obtained from the flash material image of the acquisition module, and the parameters of the material map are calculated using nonlinear optimization technology, and further, the appearance properties of the material image are obtained and the material map is generated.

Fig. 2 shows a detailed flowchart according to an embodiment of the present invention. Its specific implementation steps include S201～

S201, data collection, shooting two material images with flash and without flash through mobile phones, SLR cameras and other equipment.

S202, highlight separation, the technical solution of the present invention separates the original RGB image into two parts, a diffuse reflection map and a specular reflection map, by using the light and shade value Y of the YUV color space.

Its specific implementation is shown in (1) to (5) below, including:

(1) Calculate the original shading map Y, and the original color map U, V according to the original RGB image;

(2) The user interactively inputs the separation parameters (generally 0.6-0.8);

(3) New shading image Y2=original shading image Y*separation parameter;

(4) Generate a new RGB image according to the new light and shade map Y2 and the original color map U, V, as a diffuse reflection map;

(5) Specular reflection map = original RGB image - diffuse reflection map.

S203, normal map initialization, the basic idea is to construct S vector and T vector in tangent space according to the pixel value difference of the new light and dark map Y2, and obtain N _D from S×T. details as follows:

According to the value D(i,j) of the pixel point (i,j), the tangent vector in the direction of S and T is,

S(i,j)=(1,0,D(i+1,j)-D(i-1,j),),

T(i,j)=(0,1,D(i,j+1)-D(i,j-1),).

Therefore, the normal map can be calculated according to N _D (i,j)=S(i,j)×T(i,j).

S204, the albedo map is initialized, and the albedo=diffuse map/NdotL. Among them, NdotL is the inner product of the pixel normal vector and the illumination vector. At this stage it is assumed that the lighting is perpendicular to the surface of the material, ie L(0,0,1).

S205, the highlight index map is initialized, and the user interactively inputs highlight parameters (generally 0.1-0.5), where the highlight index map=new shading map Y2*high light parameter.

S206, initializing the specular reflectance map, specular reflectance map=specular reflectance map/pow(NdotH, g(x))/NdotL. NdotL: the inner product of the pixel normal vector and the illumination vector; NdotH: the inner product of the pixel normal vector and H (the middle component of illumination and line of sight); g(x): any pixel x highlight index; pow(NdotH,g( x)): NdotH to the power of g(x).

S207. According to the image with flash, use nonlinear optimization technology to calculate material texture parameters, including the following (1)-(3):

(1) Cluster all pixels of the flash-free image (N is the number of clusters).

(2) According to the FOV parameters of the camera, initialize the illumination Lj and line of sight Vj parameters of each pixel of the image with flash.

(3) Process each type of pixel Ci sequentially according to the following steps.

For step (3), it also includes (A) ~ (E) 6 sub-modules:

(A) Randomly select a pixel point P from the current category Ci;

(B) Read the texture map parameters corresponding to the pixel point P (obtained in steps S202-S206 according to the initialization of the flash-free image);

(C) Read the RGB value, illumination Lj, and line of sight Vj of each pixel point Pj of the current category Ci;

(D) Use the values of (B) and (C) as the initial value of the material texture parameter of the pixel point P and the known input data, and use the nonlinear optimization technology to recalculate the texture parameter of the pixel point P, and use it as the current category The texture parameter base for .

(E) Sequentially calculate the ratio of other pixels of the current category Ci to the pixel P, as the weight coefficient of other pixels, and calculate the texture parameters of other pixels according to the texture parameter base of the current category.

The above descriptions are only preferred embodiments of the present invention, and the present invention is not limited to the above-mentioned embodiments, as long as they achieve the technical effects of the present invention by the same means, they should all belong to the protection scope of the present invention. There can be various modifications and changes in its technical scheme and/or implementation within the protection scope of the present invention.

Claims (16)

1. the mutual material of a portable equipment obtains system, it is characterised in that this system includes:

Acquisition module, is used for using portable equipment to gather flashless texture map picture, is additionally operable to utilize portable equipment to gather band flash of light Texture map picture；

High light separation module, changes into YUV face for the flashless original RGB color image gathered by described acquisition module Colour space image, and separation parameter based on user's input and the original continuous-tone image of YUV color space image be calculated newly Continuous-tone image, further by YUV image isolated mirror reflection figure and diffuse-reflectance figure；

Normal direction module, poor according to the pixel that high light separation module obtains new continuous-tone image, create in tangent space the first tangent vector and Second tangent vector, further, calculates normal direction figure initial value according to the first tangent vector and the second tangent vector；

Diffuse-reflectance module, for obtaining the diffuse-reflectance figure of described high light isolated, calculates diffuse-reflectance coefficient figure initial value；

High optical index module, is used for obtaining described high light separation module and obtains new continuous-tone image, further, height based on user's input Backscatter extinction logarithmic ratio calculates the initial value of high optical index figure；

Mirror reflection module, for obtaining the mirror reflection figure of described high light isolated, calculates mirror reflection coefficient further The initial value of figure；

Parameter optimization module, for obtaining according to described normal direction module, diffuse-reflectance module, high optical index module and mirror reflection The initiation parameter that module obtains, also obtains the band flash of light texture map picture of described acquisition module, uses non-linear optimization technique meter Calculate texturing parameter and generate texturing.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described high light separates Module also includes:

Transform subblock, for being converted to YUV color space by the original RGB color of the flashless texture map picture gathered Texture map picture, original continuous-tone image Y1 and primitive color figure U and saturation V；

Segregant module, for separation parameter p arranged according to User Defined, wherein new continuous-tone image Y2 is Y2=Y1*p, enters one Step, is combined as new RGB image based on new continuous-tone image Y2, primitive color figure U and saturation V, and using new RGB image as diffuse-reflectance Figure, mirror reflection image and diffuse-reflectance figure and be original RGB image.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described normal direction artwork Block also includes:

Poor, further, for any pixel for obtaining the pixel of described new continuous-tone image Y2Value, at S and T The tangent vector in direction is respectively

,

,

Then the amassing as normal direction figure initial value of the first tangent vector S and the second tangent vector T.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described diffuse-reflectance mould Block also includes:

For diffuse-reflectance initial value is calculated, wherein,

Diffuse-reflectance coefficient figure=diffuse-reflectance figure/NdotL,

NdotL is the inner product of pixel normal vector and illumination vector.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described high optical index Module also includes:

For the high optical parameter inputted according to User Defined, the initial value of high optical index figure is calculated, its calculation For,

The continuous-tone image Y2* high optical parameter of high optical index figure=new.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described mirror reflection Module also includes:

For calculating the initial value of specularity factor figure, its calculation is,

Specularity factor figure=direct reflection figure/pow (NdotH, g (x))/NdotL,

Wherein H represents illumination, sight line Middle Component, and NdotH is the inner product of pixel normal vector and H, and g (x) represents any pixel x High optical index.

The mutual material of portable equipment the most according to claim 1 obtains system, it is characterised in that described parameter optimization Module also includes:

Cluster submodule, for all pixels of the flashless image of parameter initialization are carried out N cluster, N represents cluster Number；

Acquisition parameter submodule, for obtaining the FOV parameter of portable equipment, initializes the illumination of each pixel of band flashlight images Lj, sight line Vj parameter；

Processes pixel submodule, for each pixel class Ci process to described band flashlight images.

The mutual material of portable equipment the most according to claim 7 obtains system, it is characterised in that described processes pixel Each pixel class Ci is processed and also includes by module:

Pixel chooses submodule, for obtaining the value of any one pixel P in current pixel classification；

Read initial parameter sub-module, for obtaining the flashless texture map corresponding with pixel P as the initialization ginseng of pixel Number；

Read acquisition parameter submodule, for read the rgb value of each pixel Pj of current pixel classification Ci, illumination Lj, Sight line Vj；

Nonlinear optimization submodule, chooses submodule, reading initial parameter sub-module for transferring described pixel respectively, reads and adopt The data that collection parameter sub-module obtains, use nonlinear optimization to calculate the parameter of pixel P, and store as current pixel classification Pinup picture parameter base corresponding for Ci；

Pinup picture parameter sub-module, for calculating the residual pixel point of current pixel classification Ci and the ratio of pixel P one by one, as The weight coefficient of residual pixel point, further, according to the pinup picture parameter base of current pixel classification Ci, calculates the patch of residual pixel point Graph parameter.

9. a mutual material acquisition methods, it is characterised in that the method includes:

Acquisition module, is used for using portable equipment to gather flashless texture map picture, is additionally operable to utilize portable equipment to gather band flash of light Texture map picture；

The flashless original RGB color image gathered by described acquisition module changes into YUV color space image, and based on The separation parameter of user's input and the original continuous-tone image of YUV color space image are calculated new continuous-tone image, further will YUV image isolated mirror reflection figure and diffuse-reflectance figure；

The pixel that high light separation module obtains new continuous-tone image is poor, creates the first tangent vector and the second tangent vector in tangent space, enters one Step, calculates normal direction figure initial value according to the first tangent vector and the second tangent vector；

Obtain the diffuse-reflectance figure of described high light isolated, calculate diffuse-reflectance coefficient figure initial value；

Obtaining described high light separation module and obtain new continuous-tone image, further, high backscatter extinction logarithmic ratio based on user's input calculates high light and refers to The initial value of number figure；

Obtain the mirror reflection figure of described high light isolated, calculate the initial value of mirror reflection coefficient figure further；

Obtain the initialization obtained according to described normal direction module, diffuse-reflectance module, high optical index module and mirror reflection module Parameter, also obtains the band flash of light texture map picture of described acquisition module, uses non-linear optimization technique to calculate texturing parameter also Generate texturing.

Mutual material acquisition methods the most according to claim 9, it is characterised in that the method also includes:

The original RGB color of the flashless texture map picture gathered is converted to the texture map picture of YUV color space, original bright Secretly scheme Y1 and primitive color figure U and saturation V；

Separation parameter p arranged according to User Defined, wherein new continuous-tone image Y2 is Y2=Y1*p, further, based on new continuous-tone image Y2, primitive color figure U and saturation V are combined as new RGB image, and using new RGB image as diffuse-reflectance figure, mirror reflection image With diffuse-reflectance figure and be original RGB image.

11. mutual material acquisition methods according to claim 9, it is characterised in that the method also includes:

The pixel obtaining described new continuous-tone image Y2 is poor, further, for any pixelValue, in S and T direction Tangent vector be respectively,, Then the amassing as normal direction figure initial value of the first tangent vector S and the second tangent vector T.

12. mutual material acquisition methods according to claim 9, it is characterised in that the method also includes:

Calculating diffuse-reflectance initial value, wherein, diffuse-reflectance coefficient figure=diffuse-reflectance figure/NdotL, NdotL is pixel normal vector Inner product with illumination vector.

13. mutual material acquisition methods according to claim 9, it is characterised in that the method also includes:

According to the high optical parameter of User Defined input, calculating the initial value of high optical index figure, its calculation is, high The continuous-tone image Y2* high optical parameter of optical index figure=new.

14. mutual material acquisition methods according to claim 9, it is characterised in that the method also includes:

Calculating the initial value of specularity factor figure, its calculation is,

Specularity factor figure=direct reflection figure/pow (NdotH, g (x))/NdotL,

Wherein H represents illumination, sight line Middle Component, and NdotH is the inner product of pixel normal vector and H, and g (x) represents any pixel x High optical index.

15. mutual material acquisition methods according to claim 9, it is characterised in that the method also includes:

All pixels of the flashless image of parameter initialization are carried out N cluster, and N represents cluster number；

Obtain the FOV parameter of portable equipment, initialize the illumination Lj of each pixel of band flashlight images, sight line Vj parameter；

Each pixel class Ci process to described band flashlight images.

16. mutual material acquisition methods according to claim 15, it is characterised in that the method is also to not having individual Ci to count Calculate, including:

The value of any one pixel P in acquisition current pixel classification；

Obtain the flashless texture map corresponding with pixel P as the initiation parameter of pixel；

Read the rgb value of each pixel Pj of current pixel classification Ci, illumination Lj, sight line Vj；

Transfer described pixel respectively to choose submodule, read initial parameter sub-module, the number of reading acquisition parameter submodule acquisition According to, use nonlinear optimization to calculate the parameter of pixel P, and store the pinup picture parameter base corresponding as current pixel classification Ci；

Calculate the residual pixel point of current pixel classification Ci and the ratio of pixel P one by one, as the weight system of residual pixel point Number, further, according to the pinup picture parameter base of current pixel classification Ci, calculates the pinup picture parameter of residual pixel point.