CN103886635B - Self-adaption LOD model establishing method based on face clustering - Google Patents
Self-adaption LOD model establishing method based on face clustering Download PDFInfo
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Abstract
The invention discloses a self-adaption LOD model establishing method based on face clustering and belongs to the field of three-dimensional digital urban and geographic information system. The data volume of an LOD model is decreased. Due to the fact that the simplified LOD model obtained by utilizing the self-adaption LOD model establishing method is a progressive load, each LOD is formed by the partial face of an original model, and vertex attribute disorder is not caused. The self-adaption LOD model establishing method is high in algorithm efficiency, can be used for real-time self-adaption to generate the LOD model and effectively prevents the LOD switching from being excessive and unnatural. Texture optimization is standardized according to texture resolution ratio.
Description
Technical field
The invention belongs to three-dimensional digital city and GIS-Geographic Information System field, more particularly to a kind of LOD model construction side
Method.
Background technology
LOD（Levels of Detail, level of detail）It is that conventional threedimensional model loads thought, its core is to generate
Good LOD model and the loading strategy of LOD model.Good model optimization result can improve under identical stress model
Rendering efficiency；And for identical model, good loading mode equally can improve rendering speed.
Existing BUILDINGS MODELS method for simplifying, mostly using the method for simplifying based on free grid, in the face of deleting or merging
During face, building can be made to be deformed, effect on driving birds is not good；Secondly as BUILDINGS MODELS generally all posts data texturing,
And in most Model Simplification Method, opposite vertexes attribute considers not, method for simplifying can bring the mixed of Texture Coordinates
Disorderly, thus leading to texture mapping entanglement, significantly reduce the quality of model simplification.
The loading efficiency of LOD model is not high.When loading magnanimity model data, there is model and add in many three-dimensional platforms
Carry efficiency low, switch not smooth between LOD model, load and the slow problem of rendering speed, have impact on visual experience effect, shadow
Ring the further sector application of three-dimensional platform, how to improve the dynamic throughput speed of model and the 3 D rendering accelerating large scene
Speed needs the difficult problem solving.
Content of the invention
In view of the drawbacks described above of prior art, the technical problem to be solved is to provide a kind of digital city
In, improve the LOD model building method of large scene threedimensional model loading efficiency.
For achieving the above object, the invention provides a kind of self adaptation LOD model building method based on face cluster, by with
Lower step is carried out：
Step one, generation shape LOD；Generate and optimize texture LOD；
Described generation shape LOD executes according to the following steps：
A1, obtain the important factor in each face；
A2, the segmentation threshold determining important factor partitioning model；
A3, generation simplified model, build shape LOD；
Step 2, shape LOD is loaded using cumulative mode, load texture LOD using traditional switching mode.
Preferably, the important factor obtaining each face described in step one is carried out according to the following steps：
Each face for BUILDINGS MODELS defines its important factor for whole building model outward appearance percentage contribution, sets
The summation of the important factor in all faces is 1, sets the important factor summation of simplified model as E, 0<E≤1；For an equal amount of
Important factor summation E, obtains the combination in the face minimum using number.
Preferably, determining the segmentation threshold of important factor described in step one and partitioning model is carried out according to the following steps：
By all of face according to important factor big minispread, by set two or more important factors segmentation threshold,
Building sides is split, obtains face cluster result, thus BUILDINGS MODELS is divided into some；The portion again these being divided
Divide shape LOD being combined obtaining different stage.
Preferably, generating simplified model described in step one, building shape LOD and carrying out according to the following steps：
All faces in threshold value constitute the simplified model of this rank；By different segmentation thresholds, divide and obtain difference
The simplified model of rank, constructs shape LOD by the simplified model combination of different stage.
Preferably, carry out LOD model texture optimization described in step one carrying out according to the following steps：
Set the fine textures of LOD model texture as T1, set the simplification texture of LOD model texture as T2；
For fine textures T1, optimize refined model dimension of picture：
Set grain criteria resolution for pinup picture as R1(r1x,r1Y), r1X and r1Y is respectively the texture being used for pinup picture
Resolution on coordinate axess x direction and y direction for the standard；Set refined model photo resolution as R2(r2x,r2Y), r2X and r2y
It is respectively resolution on coordinate axess x direction and y direction for the refined model picture；Set the reduction multiple of refined model picture as
N (nx, ny), nx and ny are respectively the reduction multiple in coordinate axess x direction and y direction for the fine textures；CalculateObtain the reduction multiple of refined model picture；By refined model figure
The length and width of piece is reduced to original 1/N (nx, ny)；
Using the render to texture technology of 3Dmax, all simplification texture T2 are merged into a figure, and utilize resampling side
Method reduces and simplifies the resolution after texture T2 merges.
The invention has the beneficial effects as follows：
1, The present invention reduces the data volume of LOD model, improves the loading efficiency of model.General LOD model is at least
Including original refined model, and the simplified model generating, if multistage LOD, model value is more.And obtained using this method
Simplified model be using original refined model part form, all total model values remain as original refined model.Stricture of vagina
Reason aspect, it is all same texture for multistage LOD model that context of methods generates simplification texture, and generally produces
, due to there is deformation it may be necessary to more set texture and many set texture coordinates could meet requirement in LOD model.
2nd, the present invention will not cause the entanglement of vertex attribute；The simplification LOD model being obtained using this method is due to being a kind of
Laddering loading, the part face that the composition of each LOD derives from archetype is constituted, thus without causing vertex attribute
Entanglement.The texture coordinate that such as summit comprises, normal information will not occur entanglement after LOD generation.
3rd, inventive algorithm efficiency high, can be used for real-time adaptive and generates LOD model；LOD model texture can be fixed and set
It is set to simplification texture and fine textures two-layer, and during shape LOD generates, simplified model is to be made up of the part face of refined model
It is only necessary to according to the fine degree setting, the then size of the important factor according to face, real-time change portion facet, obtain new
Clustering, thus obtaining new shape LOD, with LOD model texture constitute LOD model.
4 present invention effectively prevents excessively not natural during LOD switching；For common LOD switching, due to simplifying mould
Type and refined model are different, are therefore to switch between different models inevitably to have inconsistent situation, simplify effect
Fruit is better, crosses and gets over nature.When switching over using this method, on the basis of the LOD model of front portion, it is continuously increased new face,
Constitute new more fine LOD model, be finally reached the display of refined model.
5th, according to fixture resolution to texture optimization more specification；For three-dimensional digital city model, not only should there is mould
Type precision, the precision that also should paste texture to model carries out unified consideration, and the present invention is carried out to texture certainly using resolution
Dynamic optimize, the more specification pinup picture standard of texture.
Brief description
Fig. 1 is the schematic flow sheet of the embodiment of the invention.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples：
As shown in figure 1, a kind of self adaptation LOD model building method based on face cluster, carry out according to the following steps：
Step one, generation shape LOD, generate and optimize texture LOD；
Described generation shape LOD executes according to the following steps：
A1, obtain the important factor in each face；
A2, the division threshold value determining important factor partitioning model；
A3, generation simplified model, build shape LOD；
Step 2, shape LOD is loaded using cumulative mode, load texture LOD using traditional switching mode.
The important factor obtaining each face described in step one is carried out according to the following steps：
Each face for BUILDINGS MODELS defines its important factor for whole building model outward appearance percentage contribution, according to
The practical significance in mould shapes itself and model face defines the important factor in face.In the present embodiment, according to mould shapes itself
Practical significance define face important factor be：The big important factor of the area in face is more than the important factor in the little face of area；According to
The important factor that the practical significance in model face defines face is：The important factor in the agent structure face of building is more than building detail moulding
The important factor in face.The summation defining the important factor in all faces is 1, sets the important factor summation of simplified model as E, 0<E
≤1；Simplified model is the combination an equal amount of important factor summation E being obtained to the face minimum using number.E is bigger, letter
Change model finer.
Determine the segmentation threshold of important factor described in step one and partitioning model is carried out according to the following steps：
By all of face according to important factor big minispread, by set two or more important factors segmentation threshold,
By BUILDINGS MODELS, coplanar is not clustered, thus being divided into some, then the part that these divide is combined obtaining not
Shape LOD of same level.In the present embodiment, segmentation threshold adopts adaptive approach to obtain, according in loaded and displayed, to this level
The requirement of the fine degree of other simplified model is setting.
Generate simplified model described in step one, build shape LOD and carry out according to the following steps：
All of face can be divided into two parts by each segmentation threshold, and all faces in threshold value constitute this rank
Simplified model；By different segmentation thresholds, divide the simplified model obtaining different stage, by the simplification mould of different stage
Shape LOD is constructed in type combination；Adjacent two-stage LOD, finer simplified model is all faces and incremental portion compared with simple model
Divide all faces（It is included in both and divide the face between threshold value）Sum.
In the present embodiment, more ranks are by that analogy taking three-level LOD model as a example.As following formula（1-4）Shown：
f1＞=f2... ＞=fk1... ＞=fk2... ＞=fn；（1）
Fall={F1,F2,……,Fk1,……,Fk2,……,Fn}；（2）
Formula（1）Be the important factor by the face arranging from big to small, this embodiment using each face size come to
Determine the value of important factor, formula（2）For the arrangement in the corresponding face of each important factor, formula（3）For face clustering result, according to
Division result obtains formula（4）, i.e. LOD model, comprise LOD0, LOD1 and LOD2.
Formula（1）Middle fiFor coming the important factor of i-th bit in sequence, i is positive integer, fk1And fk2It is composition M1 and M2 respectively
Important factor value f in face minima, i.e. segmentation threshold, subscript k1 and k2 represent its position in important factor sequence；
Formula（2）In, FiRepresent fiCorresponding face, FallRefer to the set in all faces, and refined model contains all faces, so FallI.e. essence
Thin model.M1, M2, M3 represent the face set of three parts being divided into respectively, and such as M1 is by face F1,F2... ..., Fk1Composition.
Formula（4）In, refined model LOD0 is combined by { M1, M2, M3 } and constitutes, and what LOD1 and LOD2 represented is the letter of the different stage generating
Change model, be made up of { M1, M2 } and { M1 } respectively, the fine degree of LOD1 is greater than LOD2.
Divide minima fk1And fk2Determination include the constraint of two aspects：
1）Constraints 1：
The aspect ratio that original refined model can be kept from simplified model to set, and only needs to meet in LOD mould
In the interval definition of its display, such as LOD1 is display between modal distance 500-1000 rice to type, as long as ensureing
LOD1 meets requirement in 500 meters remote of outward appearance, adaptive can obtain the first of LOD model face by given definition size
Step divides.
Taking be all triangular facet by all faces of model as a example it is assumed that important factor with the area of triangular facet as face, and with
As a example generating two-layer LOD model, comprise LOD0, and LOD1.So threshold value can determine first as follows：
VScreen=VThe world*MVPW；（5）
Above formula shows the model coordinate in world coordinate system to the transformational relation of screen coordinate system, VScreenFor screen coordinate,
VThe worldFor coordinate in world coordinate system for the point, MVPW is transition matrix.Setting screen allows the smallest triangle size ignored,
Inverse its in actual coordinates, positioned at simplified model and refined model switching apart from when triangle area size, as
The area of minimal face in simplified model set, that is, correspond to the important factor of minimal face, in formula（2）In the sequence in face representing, obtain
Must correspond to and try to achieve the P1 triangular facet as dividing interval, P1 is positive integer.
2）Constraints 2：
The restriction of only P1 is inadequate, if a model is all made up of little face, can be by Ignore All, and gained simplifies
The face collection of model is combined into sky, therefore also needs to increase P2, the setting of P2 is as follows：
By the important factor in face by progressively adding up from big to small, its calculating formula（1）In front P2 item and E（All summations
Size is 1）, E satisfaction：
fiFor the important factor of i-th triangle, EminFor allow E minima, by formula（6）The value obtaining P2 can be solved.
The size of the P1 trying to achieve in conjunction with constraint 1 and the value constraining 2 P2 trying to achieve, have：
Wherein k=max (P1, P2)；（7）
And then it is as follows to obtain the composition of shape LOD：
Generate and optimize texture LOD described in step one to carry out according to the following steps：
Set the fine textures of LOD model texture as T1, set the simplification texture of LOD model texture as T2；
For fine textures T1, optimize refined model dimension of picture：
Set grain criteria resolution for pinup picture as R1(r1x,r1Y), r1X and r1Y is respectively the texture being used for pinup picture
Resolution on coordinate axess x direction and y direction for the standard；Set refined model photo resolution as R2(r2x,r2Y), r2X and r2y
It is respectively resolution on coordinate axess x direction and y direction for the refined model picture；Set the reduction multiple of refined model picture as
N (nx, ny), nx and ny are respectively the reduction multiple in coordinate axess x direction and y direction for the fine textures；CalculateObtain the reduction multiple of refined model picture；By refined model figure
The length and width of piece is reduced to original 1/N (nx, ny).
Using the render to texture technology of 3Dmax, all simplification texture T2 are merged into a figure, and utilize resampling side
Method reduces by 3/4 or 15/16, in the present embodiment by simplifying the resolution after texture T2 merges, after the method for sampling will merge
The resolution simplifying texture T2 reduces by 3/4.
By the way of cumulative, described shape LOD and LOD model texture are loaded in step 2, the present embodiment is with three
As a example layer LOD model and two-layer LOD texture, the loading distance range of model difference LOD is as follows:
S be modal position apart from current view point distance, s1, s2 be L0D switching distance, in conjunction with LOD model generation when
Parameter, the switching distance setting during the acquisition of P1 value is as the value of s1 and s2.Equally after given switching distance,
New adaptive in real time LOD model can be obtained with real-time adjustment clustering.
As s >=s1, only load M1, texture uses T2,（T2 is to simplify texture, and T1 is fine textures, by upper one section texture
LOD generates and obtains two-layer LOD texture with optimizing）, as s1 ＞ s >=s2, increase M2, now constitute LOD1, texture still uses
T2；As s2 ＞ s, further M3 is increased to scene, form refined model LOD0, simultaneously using fine textures T1.As
This, define laddering model LOD and load system.
The present invention utilizes model LOD technology, obtains the model node of one group of different level of detail.LOD model comprises two
Point, shape LOD and texture LOD.The LOD model texture that the present embodiment is related to mainly includes fine textures and one-level resolution decreasing
Simplify texture, altogether two-layer.And shape LOD equally includes refined model and multistage simplified model, collectively form LOD model.As 2 grades
LOD model, including refined model and one-level simplified model, 3 grades of LOD models, comprises refined model and 2 grades of simplified models.Finely
Model is the threedimensional model having obtained, and can be obtained by three-dimensional manual modeling or automatic modeling.
The face of the model that the present embodiment is related to, is wholly converted into the triangular facet after triangular grid including model and comprises polygon
Shape face and the set of triangular facet.
The preferred embodiment of the present invention described in detail above.It should be appreciated that those of ordinary skill in the art is no
Need creative work just can make many modifications and variations according to the design of the present invention.Therefore, all technology in the art
It is available that personnel pass through logical analysis, reasoning, or a limited experiment under this invention's idea on the basis of existing technology
Technical scheme, all should be in the protection domain being defined in the patent claims.
Claims (4)
1. a kind of self adaptation LOD model building method based on face cluster is it is characterised in that carry out according to the following steps：
Step one, generation shape LOD；Generate and optimize texture LOD；
Described generation shape LOD executes according to the following steps：
A1, obtain the important factor in each face；
A2, the segmentation threshold determining important factor partitioning model；
A3, generation simplified model, build shape LOD；
Build shape LOD to carry out according to the following steps：
All faces in threshold value constitute the simplified model of three-level；By different segmentation thresholds, divide and obtain different stage
Simplified model, constructs shape LOD by the simplified model combination of different stage；
By three-level LOD model, such as following formula：
f1＞=f2... ＞=fk1... ＞=fk2... ＞=fn；
Fal={ F1, F2,……,Fk1,……,Fk2,......,Fn}；
f1＞=f2... ＞=fk1... ＞=fk2... ＞=fnBe by the face arranging from big to small important because
Son, gives the value of important factor, F using the size in each faceall={ F1, F2,......,Fk1,......,
Fk2,......,FnBe the corresponding face of each important factor arrangement,For face clustering knot
Really, obtained according to division resultI.e. LOD model, comprises LOD0, LOD1 and
LOD2,
fiFor coming the important factor of i-th bit in sequence, i is positive integer, fk1And fk2Respectively be composition M1 and M2 face important
The minima of factor values f, i.e. segmentation threshold, subscript k1 and k2 represent its position in important factor sequence；FiRepresent fiCorresponding
Face, FallRefer to the set in all faces, and refined model contains all faces, so FallI.e. refined model, M1, M2, M3 are respectively
Represent the face set of three parts being divided into；Refined model LOD0 is combined by { M1, M2, M3 } and constitutes, LOD1 and LOD2 represents
Be the different stage generating simplified model, respectively by { M1, M2 } and { M1 } composition, the fine degree of LOD1 is greater than
LOD2；
Step 2, shape LOD is loaded using cumulative mode, load texture LOD using traditional switching mode.
2. the self adaptation LOD model building method based on face cluster as claimed in claim 1, is characterized in that：Institute in step one
The important factor stating each face of acquisition is carried out according to the following steps：
Each face for BUILDINGS MODELS defines its important factor for whole building model outward appearance percentage contribution, sets all
The summation of the important factor in face is 1, sets the important factor summation of simplified model as E, 0<E≤1；For an equal amount of important
Factor summation E, obtains the combination in the face minimum using number.
3. the self adaptation LOD model building method based on face cluster as claimed in claim 1, is characterized in that：Institute in step one
State the segmentation threshold determining important factor and partitioning model is carried out according to the following steps：
By all of face according to important factor big minispread, by setting the segmentation threshold of two or more important factors, to building
The face building model is split, and obtains face cluster result, thus model partition is become some；The part again these being divided
It is combined obtaining shape LOD of different stage.
4. the self adaptation LOD model building method based on face cluster as claimed in claim 1, is characterized in that：Institute in step one
State and carry out LOD model texture optimization and carry out according to the following steps：
Set the fine textures of LOD model texture as T1, set the simplification texture of LOD model texture as T2；
For fine textures T1, optimize refined model dimension of picture：
Set grain criteria resolution for pinup picture as R1(r1x,r1Y), r1X and r1Y is respectively the grain criteria being used for pinup picture
Resolution on coordinate axess x direction and y direction；Set refined model photo resolution as R2(r2x,r2Y), r2X and r2Y is respectively
For resolution on coordinate axess x direction and y direction for the refined model picture；Set the reduction multiple of refined model picture as N
(nx, ny), nx and ny is respectively the reduction multiple in coordinate axess x direction and y direction for the fine textures；CalculateObtain the reduction multiple of refined model picture；By the length of refined model picture and
Width reduction is original 1/N (nx, ny)；
Render texture mapping technology using 3Dmax, all simplification texture T2 are merged into a figure, and pressed using method for resampling real
Border needs to reduce and simplifies the resolution after texture T2 merges.
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