CN103971397B - The global illumination method for drafting reduced based on virtual point source and sparse matrix - Google Patents
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Abstract
The invention discloses a kind of global illumination method for drafting reduced based on virtual point source and sparse matrix, this global illumination method for drafting uses and uses a large amount of light tree putting virtual point source model construction geometric scene to be drawn, and use cluster that Visual Sampling point is classified, classification processes all kinds of Visual Sampling points, and when each class Visual Sampling point is processed, build the light transfer matrix of such Visual Sampling point.This global illumination method for drafting combines the advantage of the method for the global illumination drafting of a large amount of point source and sparse matrix reduction, and in drawing process, light transfer matrix is divided into submatrix, by a large amount of point source problems division sub-block process, and each submatrix is carried out sparse matrix reduction thus accelerate global illumination draw effect and speed.
Description
Technical field
The present invention relates to image technique field, particularly relate to one and reduce based on virtual point source and sparse matrix
Global illumination method for drafting.
Background technology
Global illumination is very important research field in computer graphics, by illumination feelings in the Nature
The simulation of condition, catches the repeatedly propagation of the light in true environment, reflects, reflects produced soft shadow, indirectly
The lighting effects such as refraction, these effects can be greatly reinforced the sense of reality of rendering effect.This technology is usually used in electricity
In shadow, animation, the rendering of threedimensional model.Global illumination has multiple implementation method, such as radiancy, light
Follow the trail of, ambient light is covered, photon pinup picture.
The method of a large amount of point sources (Many-light) is the global illumination technology that one type is important, and it is on the scene
Scape generates a large amount of virtual point source (VirtualPointLight, VPL), adopts by calculating each visual angle respectively
The degree that sampling point (Sample) is illuminated by these virtual point source, obtains global illumination effect.By handle
Complicated repeatedly propagation problem is reduced to sampled point and is directly illuminated problem by virtual point source, asks for global illumination
Topic provides a unified mathematical framework, and has the highest motility, can adjust according to actual needs
The complexity of joint algorithm.
Drawing speed for improving further, improve real-time, Wald et al. has invented based on a large amount of point sources
The light of framework cuts (lightcuts) method, virtual point source is set up hierarchical structure and uses hierarchical structure tree
A cut set represent all virtual point source, reduce operand and also accelerate arithmetic speed.
In recent years, along with the most perfect to light segmentation method of different researcheres, a large amount of point source frameworks have become
The one the highest in order to realize efficiency in global illumination method.But, light segmentation method need nonetheless remain for carrying out in a large number
Calculating, average each visual angle sampled point needs the contribution calculating hundreds of to thousands of virtual point source to it, sternly
Heavily constrain drafting speed, poor real.It can therefore be seen that efficiency remains limits its application development
Main Bottleneck.
Summary of the invention
For the deficiencies in the prior art, the present invention proposes one and reduces based on virtual point source and sparse matrix
Global illumination method for drafting, this global illumination method for drafting greatly reduces operand, improve computing speed
Rate.
A kind of global illumination method for drafting reduced based on virtual point source and sparse matrix, including:
(1) build the space acceleration layer aggregated(particle) structure of geometric grid, and level knot is accelerated in the space described in utilization
Structure and the positional information of video camera, use Image Synthesis by Ray Tracing to determine video camera Visual Sampling in geometric grid
Point, and determine the sampling point information of each Visual Sampling point,
Described sampling point information includes position, normal vector, material and the respective pixel of corresponding Visual Sampling point
Point labelling；
(2) according to positional information, material information and the energy information of each virtual point source, light is utilized to cut
Algorithm sets up light tree；
(3) according to the position of each Visual Sampling point, Visual Sampling point is clustered, by described vision
If sampled point is divided into Ganlei；
(4) for each class Visual Sampling point, it is handled as follows:
(4-1) use light to cut algorithm according to described light tree to calculate the light of such Visual Sampling point and cut, and root
The initial light transfer matrix determining such Visual Sampling point is cut according to described light,
Described initial light transfer matrix is m × n rank, and m is the Visual Sampling point in such Visual Sampling point
Number, n is that described light cuts middle light and cuts the number of node；
(4-2) described initial light transfer matrix is carried out stochastical sampling, obtains several characteristic elements,
Calculate the value of each characteristic element, and according to the value of each characteristic element, initial light transfer matrix is carried out dimensionality reduction
Process, obtain middle light transfer matrix；
(4-3) utilize characteristic element that middle light transfer matrix is carried out sparse matrix reduction, obtain such and regard
Feel the light transfer matrix of sampled point, and using the cumulative of the element value of row element each in light transfer matrix and as
The illumination value of the Visual Sampling point that this row is corresponding；
(5) according to the corresponding pixel points labelling of each Visual Sampling point, each pixel in geometric grid is determined
The Visual Sampling point that point is corresponding, is weighted summation by the illumination value of Visual Sampling point corresponding for each pixel,
Using weighted sum result as the brightness value of this pixel.
Firstly the need of following input in the global illumination method for drafting of the present invention:
Target draws the geometric grid (the most a series of tri patchs are multiple) of scene, and each geometric grid
Normal vector, each geometric grid is made up of several pixels；A series of virtual point source, including each
The material information of virtual point source, positional information (be actually comprised in target draw the position in scene and
Direction) and energy information, and the spatial information (including position and the direction of video camera) of video camera.
When in described step (4-1), the light of each class Visual Sampling point of calculating cuts, according to such Visual Sampling
The information that in point, the sampling point information of all sampled points and each node of light tree carry uses light to cut algorithm and calculates
Obtain, it may be assumed that take with each node in the sampling point information of all sampled points in such Visual Sampling point and light tree
The information of band cuts function input as light, thus the light obtaining such Visual Sampling point cuts, and each smooth steamed sandwich contains
Several light cut node.
The corresponding Visual Sampling point of every a line in initial light transfer matrix, the corresponding light of every string cuts node,
Each element represents each light during light cuts and cuts the node contribution amount to the brightness value of corresponding Visual Sampling point.Just
In beginning light transfer matrix, each element is unknown quantity.
Described step (4-2) carries out stochastical sampling and obtains series of features element, and it is special to be calculated each
(being equivalent to become characteristic element known element, hereafter the characteristic element in step is all to levy the value of element
For the most calculated known quantity, value is clear and definite).
When described step (4-3) utilizes characteristic element that middle light transfer matrix is carried out sparse matrix reduction,
The method used is low-rank matrix adaptive method (Low-Rank Matrix Fitting), the same list of references of detailed process:
Yuan Shen,Zaiwen Wen,and Yin Zhang.Augmented Lagrangian Alternating
Direction Method for Matrix Separation based on Low-Rank Factorization.Rice
Described in CAAM Tech Report TR11-02.
The sampling point information of each Visual Sampling point includes the corresponding pixel points labelling of this Visual Sampling point, phase
With corresponding pixel points use identical corresponding pixel points labelling.This Visual Sampling point is may determine that by labelling
Corresponding pixel, i.e. obtains the corresponding relation of pixel and Visual Sampling point.
In the global illumination method for drafting of the present invention, use and use a large amount of some virtual point source model construction to wait to paint
The light tree of geometric scene processed, and use cluster that Visual Sampling point is classified, classification processes all kinds of Visual Sampling points,
And when each class Visual Sampling point is processed, build the light transfer matrix (initial light of such Visual Sampling point
Transfer matrix).So classification processes, and effectively reduces the size of light transfer matrix, reduces amount of calculation.
And for each initial light transfer matrix, by stochastical sampling select Partial Elements and calculate the part of selection with
The value of element, then utilizes Partial Elements to carry out sparse matrix reduction, is calculated each viewpoint further
Brightness value.And also light transfer matrix is carried out dimension-reduction treatment before carrying out sparse matrix reduction, reduce further
Computing cost, improves arithmetic speed, thus completes real-time rendering.
The maximum height of described light tree is 64, and maximum son node number is 1000.
After using the method for sparse reduction, need the matrix number of Practical Calculation to reduce in a large number, therefore can build
Make the virtual point source that light tree construction deeper, more accurate supports million grades, carry out high-quality scene and paint
System.
The spatial hierarchy of described geometric grid is SBVH spatial hierarchy, uses SBVH method
Foundation obtains.
SBVH (space segmentation bounding box, Spatial splits in bounding volume hierarchies,
SBVH) it is the spatial hierarchy with certain feature.
In described step (3), during cluster, the distance function of each iteration is:
Wherein, α is constant,
xkFor the position average of kth class visual angle sampled point,For the normal vector average of kth class visual angle sampled point,
K=1,2 ..., K, K are the sum of the class that iteration obtains each time,
xiThe position of the Visual Sampling point for being currently clustered,The method of the Visual Sampling point for being currently clustered
Vector, i=1,2 ..., I, I are the sum of Visual Sampling point in the sampled point of kth class visual angle.
Represent the i-th Visual Sampling point distance to the position average of kth class visual angle sampled point.
During cluster, the size of every cluster is 512～1024.
The value of constant α is 0.5～1, is used in cluster process during each iteration, command range and angle phase
To importance degree.
Iteration obtains the sum of class and determines according to cluster direction each time, if top-down cluster, then first
The total K=2 of the class that secondary iteration obtains, the total K=4 of the class that iteration obtains for the second time, recursion the most successively,
The total K=2 of the class that the l time iteration obtainsl, l=1,2 ..., L, L are iteration total degree during cluster,
Determine according to practical situation.
The present invention uses K-average (K-means) to cluster.Final is painted by the cluster of Visual Sampling point
Influential effect processed is relatively big, but uses the traditional clustering method that more conservative quality is higher, cluster uses away from
Consider position and normal direction, bigger bunch of stability when can increase matrix reduction and accuracy from function simultaneously.
The error upper limit that described step (4-1) uses light to cut when algorithm calculating light cuts is 1%～5%.
The error upper limit setting cutting in algorithm 1%～5% at traditional light is too high, although may produce higher
Picture quality, but a large amount of light can be produced and cut node thus bring a large amount of computing.In the method, use
The method of sparse reduction, can effectively reduce operand, therefore use the higher error upper limit to ensure surely
Fixed picture quality.
Described step (4-2) comprises the steps:
(4-21) initial light transfer matrix is carried out stochastical sampling for the first time and obtains several fisrt feature elements,
Ensure in every string at least two characteristic elements, and calculate the value of each characteristic element；
(4-22) variance of each row characteristic element in initial light transfer matrix is calculated respectively, and with the side of each row
The proportion that difference accounts in population variance is as the sampled probability of these row, to the every string in initial light transfer matrix,
Carry out second time stochastical sampling according to the sampled probability that each row are corresponding, obtain several second feature elements, and
Calculate the value of each characteristic element；
(4-23) using all fisrt feature elements and second feature element as characteristic element, initial light is passed
Pass each characteristic element of row maximum in matrix to compare with the threshold value of setting, and according to comparative result, from just
Beginning light transfer matrix is rejected the characteristic element row less than setting threshold value of maximum, obtains middle light transfer matrix.
After having sampled, remove the row that contribution is little, can effectively reduce the dimension of matrix, increase subsequent step
In carry out sparse matrix reduction time amount of calculation, the efficiency of raising.
Element number during the number of the characteristic element that stochastical sampling obtains is initial light transfer matrix for the first time
W1%, element number during the number of the characteristic element that stochastical sampling obtains is initial light transfer matrix for the second time
W2%, wherein w1+w2=10～15.
As preferably, w2/w1=9～12.
Characteristic element is the most, and after carrying out sparse matrix reduction, final calculated illumination value is more nearly reality
Border illumination value.But operand must also be reduced simultaneously, improve reduction efficiency, it is ensured that real-time.For to the greatest extent may be used
The accuracy increasing matrix reduction of energy, needs the useful information calculating in matrix, and the row that variance is bigger can
The information that can comprise is more (such as represents that in light tree, certain node produces the moon of complexity to a class Visual Sampling point
Shadow), therefore need to add resampling.The list that in like manner variance is relatively low shows that lighting effect is more steady.
Use light to cut algorithm and calculate the value of each characteristic element.
Such as list of references WALTER, B., A RBREE, A., BALA, K., AND GREENBERG, D.
P.2006.Multidimensional lightcuts.ACM Transactions on Graphics 25,3(July),
Method disclosed in 1081 1088., (includes fisrt feature element and second feature unit according to characteristic element
Element) corresponding light cuts the spatial information of node, material information and energy information, and the position of Visual Sampling point
Put and material, use the light technology of cutting to calculate the value (i.e. brightness value) of each characteristic element.
Threshold value in described step (4-23) is 10-4～10-5。
That chooses this value reason is that in sampled point, maximum point energy is less than the contribution to final image of the row of this value
The least, naked eyes are the most visible.But row are all that this type of contribution is the least greatly in matrix
Row, remove them from matrix and can effectively reduce matrix, accelerate the speed of matrix reduction.
Compared with prior art, beneficial effects of the present invention is as follows: combine a large amount of point source and sparse matrix also
The advantage of the method that former global illumination is drawn, and in drawing process, light transfer matrix is divided into submatrix,
A large amount of point source problems division sub-block is processed, each submatrix is carried out sparse matrix reduction thus accelerates
The effect of global illumination drafting and speed.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described in detail.
A kind of global illumination method for drafting reduced based on virtual point source and sparse matrix, including:
(1) use SBVH method to build the space acceleration layer aggregated(particle) structure of geometric grid, and utilize this space
Acceleration layer aggregated(particle) structure and the positional information of video camera, use Image Synthesis by Ray Tracing to determine that video camera is in geometric grid
Visual Sampling point, and determine the sampling point information of each Visual Sampling point,
Wherein sampling point information includes position, normal vector, material and the respective pixel of corresponding Visual Sampling point
Point labelling.
(2) according to positional information, material information and the energy information of each virtual point source, light is utilized to cut
Algorithm sets up light tree.In the present embodiment, the height of light tree is 32, and maximum son node number is 100.
(3) according to the locus of each Visual Sampling point, Visual Sampling point is clustered, by described
If Visual Sampling point is divided into Ganlei.
The present embodiment use K-average (K-means) cluster, in described step (3) during cluster
The distance function of iteration is every time:
Wherein, α is constant,
xkFor the position average of kth class visual angle sampled point,For the normal vector average of kth class visual angle sampled point,
K=1,2 ..., K, K are the sum of the class that iteration obtains each time,
xiThe position of the Visual Sampling point for being currently clustered,The method of the Visual Sampling point for being currently clustered
Vector, i=1,2 ..., I, I are the sum of Visual Sampling point in the sampled point of kth class visual angle.
Represent the distance of i-th Visual Sampling point and kth Visual Sampling point, the present embodiment
In the size of every cluster be 512, constant α is 0.5.
(4) for each class Visual Sampling point, it is handled as follows:
(4-1) use light to cut algorithm according to described light tree to calculate the light of such Visual Sampling point and cut, calculate
Time using such Visual Sampling point and light tree as input, output is the light of such Visual Sampling point and cuts, and root
Cut the initial light transfer matrix determining such Visual Sampling point according to described light, initial light transfer matrix is m × n
Rank, m is the number of the Visual Sampling point in such Visual Sampling point, and n is that light cuts middle light and cuts the number of node.
The error upper limit when light using light to cut algorithm all kinds of Visual Sampling points of calculating in the present embodiment cuts is
1%～5% (being 2% in the present embodiment).
(4-2) initial light transfer matrix is carried out stochastical sampling, obtain several characteristic elements, calculate each
The value of individual characteristic element, and according to the value of each characteristic element, initial light transfer matrix is carried out dimension-reduction treatment,
Obtain middle light transfer matrix, specifically include following steps:
(4-21) initial light transfer matrix is carried out stochastical sampling for the first time and obtains several fisrt feature elements,
Ensure in every string at least two characteristic elements, and calculate the value of each characteristic element；
(4-22) variance of each row characteristic element in initial light transfer matrix is calculated respectively, and with the side of each row
The proportion that difference accounts in population variance is as the sampled probability of these row, to the every string in initial light transfer matrix,
Carry out second time stochastical sampling according to the sampled probability that each row are corresponding, obtain several second feature elements, and
Calculate the value of each characteristic element.
The present embodiment uses light cut algorithm, cut the position of node, material according to the light that this feature element is corresponding
Information and energy information, and the position of the Visual Sampling point of correspondence and material, calculate each characteristic element (
One characteristic element and second feature element) value.
(4-23) using all fisrt feature elements and second feature element as characteristic element, initial light is passed
(in the present embodiment, this threshold value is as 10 to pass the characteristic element that in matrix, each row are maximum and the threshold value set-4) carry out
Relatively, and according to comparative result, reject maximum characteristic element less than the row setting threshold value, obtain middle light
Transfer matrix.
(4-3) utilize characteristic element that middle light transfer matrix is carried out sparse matrix reduction, obtain such and regard
The light transfer matrix of feel sampled point, and adding up the element value of row element each in light transfer matrix, with tired
Add with as the illumination value of Visual Sampling point corresponding to this row；
(5) according to the corresponding pixel points labelling of each Visual Sampling point, each pixel in geometric grid is determined
The Visual Sampling point that point is corresponding, calculates the meansigma methods of the illumination value of Visual Sampling point corresponding to each pixel,
Using the meansigma methods that obtains as the brightness value of this pixel.
The foregoing is only the preferred embodiment of the present invention, protection scope of the present invention is not limited in above-mentioned
Embodiment, every technical scheme belonging to the principle of the invention belongs to protection scope of the present invention.For this
For the technical staff in field, the some improvements and modifications carried out on the premise of without departing from the principle of the present invention,
These improvements and modifications also should be regarded as protection scope of the present invention.
Claims (9)
1. the global illumination method for drafting reduced based on virtual point source and sparse matrix, its feature exists
In, including:
(1) build the space acceleration layer aggregated(particle) structure of geometric grid, and level knot is accelerated in the space described in utilization
Structure and the positional information of video camera, use Image Synthesis by Ray Tracing to determine video camera Visual Sampling in geometric grid
Point, and determine the sampling point information of each Visual Sampling point,
Described sampling point information includes position, normal vector, material and the respective pixel of corresponding Visual Sampling point
Point labelling；
(2) according to positional information, material information and the energy information of each virtual point source, light is utilized to cut
Algorithm sets up light tree；
(3) according to the position of each Visual Sampling point, Visual Sampling point is clustered, by described vision
If sampled point is divided into Ganlei；
(4) for each class Visual Sampling point, it is handled as follows:
(4-1) use light to cut algorithm according to described light tree to calculate the light of such Visual Sampling point and cut, and root
The initial light transfer matrix determining such Visual Sampling point is cut according to described light,
Described initial light transfer matrix is m × n rank, and m is the Visual Sampling point in such Visual Sampling point
Number, n is that described light cuts middle light and cuts the number of node；
(4-2) described initial light transfer matrix is carried out stochastical sampling, obtains several characteristic elements,
Calculate the value of each characteristic element, and according to the value of each characteristic element, initial light transfer matrix is carried out dimensionality reduction
Process, obtain middle light transfer matrix, specifically include following steps:
(4-21) initial light transfer matrix is carried out stochastical sampling for the first time and obtains several fisrt feature elements,
Ensure in every string at least two characteristic elements, and calculate the value of each characteristic element；
(4-22) variance of each row characteristic element in initial light transfer matrix is calculated respectively, and with the side of each row
The proportion that difference accounts in population variance is as the sampled probability of these row, to the every string in initial light transfer matrix,
Carry out second time stochastical sampling according to the sampled probability that each row are corresponding, obtain several second feature elements, and
Calculate the value of each characteristic element；
(4-23) using all fisrt feature elements and second feature element as characteristic element, initial light is passed
Pass each characteristic element of row maximum in matrix to compare with the threshold value of setting, and according to comparative result, from just
Beginning light transfer matrix is rejected the characteristic element row less than setting threshold value of maximum, obtains middle light transfer matrix；
(4-3) utilize characteristic element that middle light transfer matrix is carried out sparse matrix reduction, obtain such and regard
Feel the light transfer matrix of sampled point, and using the cumulative of the element value of row element each in light transfer matrix and as
The illumination value of the Visual Sampling point that this row is corresponding；
(5) according to the corresponding pixel points labelling of each Visual Sampling point, each pixel in geometric grid is determined
The Visual Sampling point that point is corresponding, is weighted summation by the illumination value of Visual Sampling point corresponding for each pixel,
Using weighted sum result as the brightness value of this pixel.
2. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that the spatial hierarchy of described geometric grid is SBVH spatial hierarchy.
3. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that in described step (3), during cluster, the distance function of each iteration is:
Wherein, α is constant,
xkFor the position average of kth class visual angle sampled point,For the normal vector average of kth class visual angle sampled point,
K=1,2 ..., K, K are the sum of the class that iteration obtains each time,
xiThe position of the Visual Sampling point for being currently clustered,The method of the Visual Sampling point for being currently clustered
Vector, i=1,2 ..., I, I are the sum of Visual Sampling point in the sampled point of kth class visual angle.
4. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that the error upper limit that described step (4-1) uses light to cut when algorithm calculating light cuts is
1%～5%.
5. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that the number of the characteristic element that stochastical sampling obtains is initial light transfer matrix for the first time
The w1% of middle element number, the number of the characteristic element that stochastical sampling obtains is that initial light transmits square for the second time
The w2% of element number in Zhen, wherein w1+w2=10～15.
6. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 5 is drawn
Method, it is characterised in that w2/w1=9～12.
7. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that the threshold value in described step (4-23) is the total luminous energy of total virtual point source
0.1%-1%.
8. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that use light to cut algorithm and calculate the value of each characteristic element.
9. the global illumination reduced based on virtual point source and sparse matrix as claimed in claim 1 is drawn
Method, it is characterised in that the weighted sum in described step (5) is average summation.
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| CN105825545B (en) * | 2016-03-29 | 2018-06-19 | 浙江大学 | The global illumination method for drafting restored based on virtual light source and adaptive sparse matrix |
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| "基于空间聚类增强Lightcuts的光照计算";王光伟等;《计算机学报》;20131130;第36卷(第11期);全文 * |
| Multidimensional Lightcuts;Bruce Walter等;《ACM Transactions on Graphics》;20060731;第25卷(第3期);全文 * |
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| CN103971397A (en) | 2014-08-06 |
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