CN102855661A - Large-scale forest scene quick generation method based on space similarity - Google Patents

Abstract

The invention discloses a large-scale forest scene quick generation method based on space similarity. The method comprises the steps of 1) setting initialization parameters of a large-scale forest simulation scene, and acquiring scene distribution information of visible data and forest space of the whole scene; 2) partitioning the space data of the large-scale forest scene based on a quadtree, and dividing the scene into blocks with the same size; 3) calculating the space similarity of the forest scene; 4) judging the similarity between the scene blocks to quickly obtain biological amounts of plants; and 5) if the similarity between the scene blocks does not reach a certain proportion, calculating a growth model of the scene blocks, determining basic plants according to initial parameter data transmitted by a user, calculating an influence range and the biological amounts of the basic plants, then introducing a three-dimensional tree model into simulation for realizing the forest scene, and displaying a visible result to the user. The method is high in speed and high in precision.

Description

Extensive scale Forest Scene rapid generation based on spatial similarity

Technical field

The present invention relates to the scale Forest Scene generation technique, especially a kind of scale Forest Scene generation method.

Background technology

Because the complicacy of general layout, process and the mutual relationship of scale Forest Scene is difficult to it is directly tested, adopting computing machine to set up forest growth model base is an effective approach.Forest growth model base need to be considered dynamic growth feature and the environmental impact of plant, comes pre-measuring plants in growth tendency and the variation in all ages and classes stage, and the Forest Growth situation is simulated.Extensive scale Forest Scene dynamic simulation not only will be stored the information of each trees, but also to calculate competition between trees interphase interaction and all trees and mutually beneficial combined influence, its computation process is complicated, calculated amount is also very huge, and the application and research of extensive Forest Growth emulation has been subject to great restriction.

When scale Forest Scene visual, be used at present the technology of visual modeling to mainly contain level of detail model (LOD) and image-based method for drafting.The level of detail technology is based on how much method for drafting, well the geometric model structure of expression tree.The image-based method for drafting is the Real-Time Rendering that the another kind of in recent years people's extensive concern is realized height sense of reality virtual scene, its advantage is to use texture to replace real model, thereby can reduce on a large scale the complexity of model, but compare with the model based on how much, distortion occurs in visual effect.Visual in order further to accelerate scale Forest Scene, existing visual simulating technology also realizes the dynamic dispatching of data in conjunction with methods such as visibility cutting, data pre-fetchings, reduce EMS memory occupation, improves and draws efficient.

Summary of the invention

In order to overcome the deficiency that rapidity is relatively poor, accuracy is relatively poor of existing scale Forest Scene generation method, the invention provides the extensive scale Forest Scene rapid generation based on spatial similarity that a kind of rapidity is good, accuracy is strong.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of extensive scale Forest Scene rapid generation based on spatial similarity, described rapid generation may further comprise the steps:

1) extensive forest simulating scenes carries out the initiation parameter setting, and from the external memory database, obtaining the scene distribution information of whole environment Visualization data and forest space according to set initiation parameter, described initiation parameter comprises: spacing, growth time and the envirment factor of the quantity of trees, the kind of trees, initial age, tree; Described envirment factor comprises: sunlight, temperature, moisture and soil; Described environment Visualization data comprise: terrain data, terrain texture, the atural object data except trees and Sky Scene data; The scene distribution information of described forest space comprises: the position of trees and initial influence circle size;

2) extensive scale Forest Scene spatial data is carried out based on the cutting apart of quaternary tree scene partitioning being become equal-sized, and set up the block message concordance list of an interior external memory scheduling, be used for the status information of all scene pieces of record;

3) spatial similarity of scale Forest Scene calculated, and recorded similarity between each piecemeal with two-dimensional array, if similarity reaches certain proportion between the scene piece, the as calculated good piecemeal that then can be used in the internal memory replaces to be calculated; If the similarity between the scene piece does not reach certain proportion, then process and store passing initial parameter and the knowledge of coming in, and these parameters after will processing obtain the growth result of plant for the growth model that calculates plant;

4) similarity between the judgement world subdivision, if similarity reaches certain proportion between the world subdivision, and the growth model of one of them scene piece has been finished calculating, can replace the not scene piece of calculating with the data of calculating the scene piece so, and need not recomputate the scene piece, thereby obtain fast the biomass of plant;

5) if the similarity between the world subdivision does not reach certain proportion, the growth model that then carries out the scene piece calculates, determine basic strain by the initial parameter data that the user transmits into, calculate influence circle scope and the biomass of basic strain, then the Three-dimension Tree model is imported the emulation that realizes scale Forest Scene, visualization result is presented to the user.

Technical conceive of the present invention is: existing method has been accelerated that forest growth model base calculates and is visual, but these methods do not take full advantage of the as calculated good data in internal memory.If can be by the similarity between the scene, take full advantage of in internal memory good as calculated data and substitute current calculative data, simulation velocity will improve greatly so.The scene of the zones of different of Forest Growth emulation may have similar growing environment, can there be certain similarity between the scene, so when the Forest Growth simulation accuracy is less demanding, can utilize this similarity to accelerate the process of the calculating of plant growth model and visual drafting, and take full advantage of the as calculated good data in internal memory, improve memory usage, thereby reached the purpose that forest emulation is accelerated.

Beneficial effect of the present invention is mainly manifested in: 1, the extensive scale Forest Scene rapid generation based on spatial similarity can carry out analysis and the judgement of spatial similarity from index of similarities such as plant growth density, terrain feature and envirment factors respectively.When the space similarity of two scene pieces reaches certain value, and wherein one growth model has been finished calculating, can replace the not scene piece of calculating with the data of calculating the scene piece so, and need not recomputate the scene piece, thereby reach the purpose that forest emulation is accelerated.

2, the method can be selected different similarity thresholds to the distance of scene piece automatically according to viewpoint, realizes that adaptive similarity threshold is definite, and assurance scene piece is replaced has higher accuracy.From the scene piece of viewpoint close to more, its needed details will be abundanter, and the sense of reality is stronger, and other piece that can replace so this piece piecemeal must need with the similarity of this piece higher; Otherwise from the piece of viewpoint away from more, the user does not need to know in sufficient detail details, only need to have a sensory effects true to nature a little get final product to scene, can replace so other piecemeal of this piece piecemeal and follow the similarity of this piece piecemeal to reduce.

Description of drawings

Fig. 1 is based on the scale Forest Scene simulation flow figure of spatial similarity.

Fig. 2 is based on the synoptic diagram of the scene cutting procedure of quaternary tree.

Embodiment

The invention will be further described below in conjunction with accompanying drawing.

See figures.1.and.2, a kind of extensive scale Forest Scene rapid generation based on spatial similarity may further comprise the steps:

1), extensive forest simulating scenes carries out the initiation parameter setting, and obtain the scene distribution information of whole environment Visualization data and forest space according to set initiation parameter from the external memory database.Described initiation parameter comprises: spacing, growth time and the envirment factor of the quantity of trees, the kind of trees, initial age, tree.Described envirment factor comprises: sunlight, temperature, moisture and soil; Described environment Visualization data comprise: terrain data, terrain texture, the atural object data except trees and Sky Scene data; The scene distribution information of described forest space comprises: the position of trees and initial influence circle size.

2), extensive scale Forest Scene spatial data is carried out based on the cutting apart of quaternary tree scene partitioning being become equal-sized, and set up the block message concordance list of an interior external memory scheduling, be used for the status information of all scene pieces of record.

World subdivision is the basis of judging similarity between each piecemeal.The scale Forest Scene dividing method as shown in Figure 2, at first the user sets the breadth extreme of piece, this value is the basis for estimation of landform being carried out four minutes recurrence; Then, use top-down mode, whole scale Forest Scene as root node, is judged that from root node whether the width of colony area surpasses the breadth extreme that the user sets, then do not cut apart and as leafy node if do not satisfy, and the relevant information of node is preserved; Otherwise, the continuous recurrence of root node is cut apart 4 equal sub-node zones, same recurrence is cut apart if this node has sibling, until no longer satisfy the condition of cutting apart.At last all leafy nodes all are kept in the external memory.

In the Forest Growth simulation process, set up the block message concordance list of an interior external memory scheduling, be used for the status information of all scene pieces of record, and dynamically update according to the variation of current view point region parameter.With one dimension array of pointers B[L] come the memory address of store data piece, as B[i]=0 the time, represent that the data of i piece are not written into internal memory; As B[i] ≠ 0 the time, B[i] the first address of memory field, value representation data block place, can be expressed as:

And 0≤i≤L-1, n ∈ N ⁺

3), the spatial similarity of scale Forest Scene is calculated, and record similarity between each piecemeal with two-dimensional array, if similarity reaches certain proportion between the scene piece, the as calculated good piecemeal that then can be used in the internal memory replaces to be calculated; If the similarity between the scene piece does not reach certain proportion, then process and store passing initial parameter and the knowledge of coming in, and these parameters after will processing obtain the growth result of plant for the growth model that calculates plant.The index of similarity is considered from these several aspects of plant growth density, terrain feature and envirment factor.

(a) density similarity.With the area of distribution space with count than the density of regarding point group as, and utilize the some population density of the trees in different distributions space to judge its similarity.In pure forest plantation situation, the trees quantity of object space 1 is counted N ₁, the shared area in distributed areas is designated as S ₁The trees quantity of object space 2 is counted N ₂, the shared area in distributed areas is designated as S ₂, the density similarity is defined as:

$\mathrm{Sim}\left(\mathrm{den}\right)=\min \{\frac{S_1/N_1}{S_2/N_2},\frac{S_2/N_2}{S_1/N_1}\}---\left(2\right)$

Under mongrel is planted situation, the seeds 1 trees quantity of object space 1 is counted N ₁₁, seeds 2 trees quantity are counted N ₁₂, the shared area in distributed areas is designated as S ₁, the seeds 1 trees quantity of object space 2 is counted N ₂₁, seeds 2 trees quantity are counted N ₂₂, the shared area in distributed areas is designated as S ₂, then the density similarity can be defined as:

Sim(den)=max{Sim(den1),Sim(den2),Sim(den12)} （3）

Wherein, $\mathrm{Sim}\left(\mathrm{den}12\right)=\min \{\frac{S_1/(N_{11}+N_{12})}{S_2/(N_{21}+N_{22})},\frac{S_2/(N_{21}+N_{22})}{S_1/(N_{11}+N_{12})}\},$

$\mathrm{Sim}\left(\mathrm{den}1\right)=\min \{\frac{S_1/N_{11}}{S_2/N_{21}},\frac{S_2/N_{21}}{S_1/N_{11}}\},$ $\mathrm{Sim}\left(\mathrm{den}2\right)=\min \{\frac{S_1/N_{12}}{S_2/N_{22}},\frac{S_2/N_{22}}{S_1/N_{12}}\}.$

(b) direction similarity.The feature on slope is expressed with the direction of landform, determines that according to the leg-of-mutton normal vector that consists of scene piece landform its direction, concrete grammar are to obtain all normal vectors that consist of world subdivision, and it is averaging.If the topographical direction angle of distribution space 1 is angle ₁, the topographical direction angle of distribution space 2 is angle ₂, the direction similarity is defined as:

$\mathrm{Sim}\left(\mathrm{dir}\right)=\min \{\frac{{\mathrm{angle}}_1}{{\mathrm{angle}}_2},\frac{{\mathrm{angle}}_2}{{\mathrm{angle}}_1}\}---\left(4\right)$

(c) area similarity.Consider the height fluctuating situation of landform by the area similarity, the landform of scale Forest Scene generally represents by triangular mesh, realize expression to the space group targeted graphical by the triangulation network of setting up landform, can calculate the surface area that the terrain mesh of scene piece forms according to the computing formula of triangle area.S ₁Be the surface area in target distribution space 1, S ₂Be the surface area in target distribution space 2, the area ratio of similitude in two target distribution spaces is:

$\mathrm{Sim}\left(\mathrm{area}\right)=\min \{\frac{s_1}{s_2},\frac{s_2}{s_1}\}---\left(5\right)$

(d) environment similarity.Analyze by the mean sea level of scene piece, average two factors of soil thickness, establish two relatively scene piece P of similarity ₁, P ₂Mean sea level be respectively H ₁And H ₂, average soil thickness is respectively T ₁And T ₂, spatial similarity is expressed as:

$\mathrm{Sim}\left(\mathrm{env}\right)=\left\{\begin{array}{cc}0& \mathrm{if}|H_1-H_2|>\mathrm{MaxH\; or}|T_1-T_2|>\mathrm{MaxT}\\ 1& \mathrm{otherwise}\end{array}\right.---\left(6\right)$

Wherein, MaxH is poor for the sea level elevation that allows between the scene piece that compares similarity, is illustrated in the poor scope of this sea level elevation, and the growing state of two scene pieces is more similar.MaxT is the difference that compares the thickness of soil that allows between the scene piece of similarity, is illustrated in the poor scope of this thickness of soil, and thickness of soil is smaller on the impact of the growing state of two scene pieces.Sim (env) value is 0 o'clock, expression scene piece P ₁And P ₂Dissimilar; Its value is 1 o'clock, represents two scene piece P ₁And P ₂Spatial similarity.

(e) comprehensive similarity.The growing space target of forest community is done as a whole, and these several respects such as the comprehensive density of trees, terrain feature, envirment factor consider.The comprehensive similarity computing method are:

Sim(space)=k ₁*Sim(den)+k ₂*Sim(area)+k ₃*Sim(dir)+k ₄*Sim(env) （7）

Wherein, Sim (den) is the density similarity, and Sim (area) is the area similarity, and Sim (dir) is the direction similarity, and Sim (env) is the similarity of envirment factor.k ₁, k ₂, k ₃, k ₄Be respectively the weight coefficient of density, area, direction, envirment factor, can set according to the simulating scenes of reality the value of weight coefficient, and k ₁+ k ₂+ k ₃+ k ₄=1.

The employing two-dimensional array is deposited the similarity between the piecemeal, if the scene number of blocks of the scene tree bottom layer node that obtains after cutting apart is L, in the table 1 in the array each A[i] [j] (0≤i＜L, similarity between the expression i piece of 0≤j＜L) and the j piece, the value of each that the i-1 in the table is capable has represented respectively the similarity between i blocks of data piece and other piece.The calculating of similarity is to carry out in the pre-service, so do not need to expend extra time.

The storage organization of table 1 similarity

4), the similarity between the judgement world subdivision, if similarity reaches certain proportion between the world subdivision, and the growth model of one of them scene piece has been finished calculating, can replace the not scene piece of calculating with the data of calculating the scene piece so, and need not recomputate the scene piece, thereby obtain fast the biomass of plant.

Suppose between the piecemeal in the scene similarity threshold from small to large, be designated as respectively SIM ₁, SIM ₂..., SIM _n, by the distance decision similarity threshold of viewpoint to piecemeal, be formulated as:

$\mathrm{SIM}=\left\{\begin{array}{c}{\mathrm{SIM}}_1,d_1\&le;d\\ {\mathrm{SIM}}_2,d_2\&le;d<d_1\\ ......\\ \mathrm{SIMn},d_n\&le;d<d_{n-1}\end{array}\right.---\left(8\right)$

In the formula, 0≤d _n＜...＜d ₂＜d ₁, d _i(i=1,2 ..., n) expression is chunked into the distance threshold of viewpoint, and d represents that viewpoint arrives the actual range of piecemeal.In order to describe this information, with a two-dimensional array C[2] relation of [n] recording distance and similarity threshold, it is as shown in table 2 that above-mentioned relation is converted into array, and the first row of array represents distance threshold, the second line display similarity threshold.

d 1	d 2	d 3	d 4	......	d n
						SIM 1	SIM 2	SIM 3	SIM 4	......	SIM n

The relation of table 2 distance and similarity threshold

Utilize the spatial similarity of scene as follows to the algorithm steps that certain scene piece carries out emulation:

Step1: suppose that the current scene piece is piece i, if B[i] be not 0, then with B[i] then assignment jump to step 6 to the tmp pointer variable, otherwise enter step 2.

Step2: computing block i and viewpoint apart from d.

Step3: the first row of search two-dimensional array C finds first its value less than or equal to the item C[0 of d] [n], accordingly C[1] [n] be the minimum similarity value that can replace.

Step4: the i of traversal array A is capable, seeks and satisfies value more than or equal to C[1] B[k of [n] and correspondence] be not 0 maximal term A[i] [k], if find this Xiang Ze with B[k] be assigned to tmp, then jump to step 6, otherwise enter step 5.

Step5: read the data of i piece from external memory to internal memory, and the address that will deposit this blocks of data is assigned to B[i] and tmp.

Step6: the memory field sense data of pointing to from tmp is also carried out emulation.

5) if the similarity between the world subdivision does not reach certain proportion, the growth model that then carries out the scene piece calculates, determine basic strain by the initial parameter data that the user transmits into, calculate influence circle scope and the biomass of basic strain, then the Three-dimension Tree model is imported the emulation that realizes scale Forest Scene, visualization result is presented to the user.

When user's viewpoint changes, draw thread and calculate current visibility region according to current view point, simultaneously the contextual data in the internal memory is judged, confirm that scene repaints needed data whether in internal memory, if contextual data in internal memory, is then directly carried out the drafting of scene and is sent the viewpoint updating message to data prefetching thread.If not in internal memory, drawing thread, the current scene that needs to draw then need the position according to current view point, the view information such as direction of current view point that the trees in the visibility region are carried out LOD calculating.

When selecting the trees LOD model of extensive scale Forest Scene, adopt three-dimensional refined model rendering technique in the extremely near-sighted point range.Because near viewpoint, viewpoint changes its drafting impact very large in the roam procedure.Simultaneously, near-sighted point range is limited by field range, and the tree of drawing simultaneously can be not too many, generally about 2,3, so can pass through the comparatively meticulous model of computer drawing.Short range adopts how much LOD models simplifying.Owing to apart from viewpoint certain distance is arranged, the change of viewpoint can not require frequent the change to draw simultaneously.From the trees of viewpoint away from more, its LOD model accuracy is more coarse.Far range because the demand of drafting individuality is not obvious, adopts the billboard Texture Mapping Technology.

Claims (5)

1. extensive scale Forest Scene rapid generation based on spatial similarity, it is characterized in that: described rapid generation may further comprise the steps:

1) extensive forest simulating scenes carries out the initiation parameter setting, and from the external memory database, obtaining the scene distribution information of whole environment Visualization data and forest space according to set initiation parameter, described initiation parameter comprises: spacing, growth time and the envirment factor of the quantity of trees, the kind of trees, initial age, tree;

Described envirment factor comprises: sunlight, temperature, moisture and soil; Described environment Visualization data comprise: terrain data, terrain texture, the atural object data except trees and Sky Scene data; The scene distribution information of described forest space comprises: the position of trees and initial influence circle size;

2) extensive scale Forest Scene spatial data is carried out based on the cutting apart of quaternary tree scene partitioning being become equal-sized, and set up the block message concordance list of an interior external memory scheduling, be used for the status information of all scene pieces of record;

3) spatial similarity of scale Forest Scene calculated, and recorded similarity between each piecemeal with two-dimensional array, if similarity reaches certain proportion between the scene piece, the as calculated good piecemeal that then can be used in the internal memory replaces to be calculated; If the similarity between the scene piece does not reach certain proportion, then process and store passing initial parameter and the knowledge of coming in, and these parameters after will processing obtain the growth result of plant for the growth model that calculates plant;

4) similarity between the judgement world subdivision, if similarity reaches certain proportion between the world subdivision, and the growth model of one of them scene piece has been finished calculating, can replace the not scene piece of calculating with the data of calculating the scene piece so, and need not recomputate the scene piece, thereby obtain fast the biomass of plant;

5) if the similarity between the world subdivision does not reach certain proportion, the growth model that then carries out the scene piece calculates, determine basic strain by the initial parameter data that the user transmits into, calculate influence circle scope and the biomass of basic strain, then the Three-dimension Tree model is imported the emulation that realizes scale Forest Scene, visualization result is presented to the user.

2. the extensive scale Forest Scene rapid generation based on spatial similarity as claimed in claim 1, it is characterized in that: described step 2), the world subdivision process is as follows: at first the user sets the breadth extreme of piece, and this value is the basis for estimation of landform being carried out four minutes recurrence; Then, use top-down mode, whole scale Forest Scene as root node, is judged that from root node whether the width of colony area surpasses the breadth extreme that the user sets, then do not cut apart and as leafy node if do not satisfy, and the relevant information of node is preserved; Otherwise, the continuous recurrence of root node is cut apart 4 equal sub-node zones, same recurrence is cut apart if this node has sibling, cuts apart condition until no longer satisfy, and all is kept at all leafy nodes in the external memory at last;

In the Forest Growth simulation process, set up the block message concordance list of an interior external memory scheduling, be used for the status information of all scene pieces of record, and dynamically update according to the variation of current view point region parameter; With one dimension array of pointers B[L] come the memory address of store data piece, as B[i]=0 the time, represent that the data of i piece are not written into internal memory; As B[i] ≠ 0 the time, B[i] the first address of memory field, value representation data block place, can be expressed as:

And 0≤i≤L-1, n ∈ N ⁺

3. the extensive scale Forest Scene rapid generation based on spatial similarity as claimed in claim 1 or 2, it is characterized in that: in the described step 3), the index of similarity comprises plant growth density similarity, direction similarity, area similarity and environment similarity, wherein

(a) plant growth density similarity: with the area of distribution space with count than the density of regarding point group as, and utilize the some population density of the trees in different distributions space to judge its similarity; In pure forest plantation situation, the trees quantity of object space 1 is counted N ₁, the shared area in distributed areas is designated as S ₁, the trees quantity of object space 2 is counted N ₂, the shared area in distributed areas is designated as S ₂, the density similarity is defined as:

$\mathrm{Sim}\left(\mathrm{den}\right)=\min \{\frac{S_1/N_1}{S_2/N_2},\frac{S_2/N_2}{S_1/N_1}\}---\left(2\right)$

Under mongrel is planted situation, the seeds 1 trees quantity of object space 1 is counted N ₁₁, seeds 2 trees quantity are counted N ₁₂, the shared area in distributed areas is designated as S ₁The seeds 1 trees quantity of object space 2 is counted N ₂₁, seeds 2 trees quantity are counted N ₂₂, the shared area in distributed areas is designated as S ₂, then the density similarity is defined as:

Sim(den)=max{Sim(den1),Sim(den2),Sim(den12)} （3）

Wherein, $\mathrm{Sim}\left(\mathrm{den}12\right)=\min \{\frac{S_1/(N_{11}+N_{12})}{S_2/(N_{21}+N_{22})},\frac{S_2/(N_{21}+N_{22})}{S_1/(N_{11}+N_{12})}\},$

$\mathrm{Sim}\left(\mathrm{den}1\right)=\min \{\frac{S_1/N_{11}}{S_2/N_{21}},\frac{S_2/N_{21}}{S_1/N_{11}}\},$ $\mathrm{Sim}\left(\mathrm{den}2\right)=\min \{\frac{S_1/N_{12}}{S_2/N_{22}},\frac{S_2/N_{22}}{S_1/N_{12}}\};$

(b) direction similarity: the feature on slope is expressed with the direction of landform, determines that according to the leg-of-mutton normal vector that consists of scene piece landform its direction, concrete grammar are to obtain all normal vectors that consist of world subdivision, and it is averaging; If the topographical direction angle of distribution space 1 is angle ₁, the topographical direction angle of distribution space 2 is angle ₂, the direction similarity is defined as:

$\mathrm{Sim}\left(\mathrm{dir}\right)=\min \{\frac{{\mathrm{angle}}_1}{{\mathrm{angle}}_2},\frac{{\mathrm{angle}}_2}{{\mathrm{angle}}_1}\}---\left(4\right)$

(c) area similarity: the height fluctuating situation of considering landform by the area similarity, the landform of scale Forest Scene represents by triangular mesh, realize expression to the space group targeted graphical by the triangulation network of setting up landform, can calculate the surface area that the terrain mesh of scene piece forms according to the computing formula of triangle area; S ₁Be the surface area in target distribution space 1, S ₂Be the surface area in target distribution space 2, the area ratio of similitude in two target distribution spaces is:

$\mathrm{Sim}\left(\mathrm{area}\right)=\min \{\frac{s_1}{s_2},\frac{s_2}{s_1}\}---\left(5\right)$

(d) environment similarity: analyze by the mean sea level of scene piece, average two factors of soil thickness, establish two relatively scene piece P of similarity ₁, P ₂Mean sea level be respectively H ₁And H ₂, average soil thickness is respectively T ₁And T ₂, spatial similarity is expressed as:

Wherein, MaxH is poor for the sea level elevation that allows between the scene piece that compares similarity, is illustrated in the poor scope of this sea level elevation, and the growing state of two scene pieces is more similar; MaxT is the difference that compares the thickness of soil that allows between the scene piece of similarity, is illustrated in the poor scope of this thickness of soil, and thickness of soil is smaller on the impact of the growing state of two scene pieces; Sim (env) value is 0 o'clock, expression scene piece P ₁And P ₂Dissimilar; Its value is 1 o'clock, represents two scene piece P ₁And P ₂Spatial similarity;

(e) comprehensive similarity: the growing space target of forest community is done as a whole, and the comprehensive similarity computing method are:

Sim(space)=k ₁*Sim(den)+k ₂*Sim(area)+k ₃*Sim(dir)+k ₄*Sim(env) （7）

Wherein, Sim (den) is the density similarity, and Sim (area) is the area similarity, and Sim (dir) is the direction similarity, and Sim (env) is the similarity of envirment factor; k ₁, k ₂, k ₃, k ₄Be respectively the weight coefficient of density, area, direction, envirment factor, can set according to the simulating scenes of reality the value of weight coefficient, and k ₁+ k ₂+ k ₃+ k ₄=1;

The employing two-dimensional array is deposited the similarity between the piecemeal, if the scene number of blocks of the scene tree bottom layer node that obtains after cutting apart is L, in the table 1 in the array each A[i] [j] (0≤i＜L, similarity between the expression i piece of 0≤j＜L) and the j piece, the value of each that the i-1 in the table is capable has represented respectively the similarity between i blocks of data piece and other piece.

4. the extensive scale Forest Scene rapid generation based on spatial similarity as claimed in claim 3 is characterized in that: in the described step 4), suppose that the similarity threshold between the piecemeal in the scene is designated as respectively SIM from small to large ₁, SIM ₂..., SIM _n, by the distance decision similarity threshold of viewpoint to piecemeal, be formulated as:

$\mathrm{SIM}=\left\{\begin{array}{c}{\mathrm{SIM}}_1,d_1\&le;d\\ {\mathrm{SIM}}_2,d_2\&le;d<d_1\\ ......\\ \mathrm{SIMn},d_n\&le;d<d_{n-1}\end{array}\right.---\left(8\right)$

In the formula, 0≤d _n＜...＜d ₂＜d ₁, d _i(i=1,2 ..., n) expression is chunked into the distance threshold of viewpoint, and d represents that viewpoint arrives the actual range of piecemeal; In order to describe this information, with a two-dimensional array C[2] relation of [n] recording distance and similarity threshold;

Utilize the spatial similarity of scene as follows to the algorithm steps that certain scene piece carries out emulation:

Step1: suppose that the current scene piece is piece i, if B[i] be not 0, then with B[i] then assignment jump to Step6 to the tmp pointer variable, otherwise enter Step2;

Step2: computing block i and viewpoint apart from d;

Step3: the first row of search two-dimensional array C finds first its value less than or equal to the item C[0 of d] [n], accordingly C[1] [n] be the minimum similarity value that can replace;

Step4: the i of traversal array A is capable, seeks and satisfies value more than or equal to C[1] B[k of [n] and correspondence] be not 0 maximal term A[i] [k], if find this Xiang Ze with B[k] be assigned to tmp, then jump to Step6, otherwise enter Step5;

Step5: read the data of i piece from external memory to internal memory, and the address that will deposit this blocks of data is assigned to B[i] and tmp;

Step6: the memory field sense data of pointing to from tmp is also carried out emulation.

5. the extensive scale Forest Scene rapid generation based on spatial similarity as claimed in claim 3, it is characterized in that: in the described step 5), when user's viewpoint changes, draw thread and calculate current visibility region according to current view point, simultaneously the contextual data in the internal memory is judged, confirm that scene repaints needed data whether in internal memory, if contextual data in internal memory, is then directly carried out the drafting of scene and is sent the viewpoint updating message to data prefetching thread; If not in internal memory, drawing thread, the current scene that needs to draw then need the position according to current view point, the view information such as direction of current view point that the trees in the visibility region are carried out LOD calculating.

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