CN102254338B - Automatic obtaining method of three-dimensional scene optimal view angle based on maximized visual information - Google Patents
Automatic obtaining method of three-dimensional scene optimal view angle based on maximized visual information Download PDFInfo
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Abstract
The invention provides an automatic obtaining method of a three-dimensional scene optimal view angle based on maximized visual information. The surface geometrical characteristic of a scene is represented through utilizing the curvature of all vertices of the three-dimensional scene, and the characteristic area of the three-dimensional scene is solved through utilizing a self-adaptive clustering method. As for each sampling view point, the visual characteristic total amount and the visual display effect on all characteristic areas are estimated, and the optimal view angle for observing the three-dimensional scene is found out finally on the basis of a clustering algorithm and a statistical method. In the method, the surface geometrical characteristic information of the given three-dimensional scene is fully utilized, the visual quality of each characteristic area is taken into consideration when the maximized visual information amount is obtained, the optimal view angle is solved, and the visual demand for people to observe the three-dimensional scene is fully met.
Description
Technical field
The present invention relates to a kind of electronic 3-D model disposal route, be specifically related to a kind of automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information.
Background technology
In recent years, Along with computer technology and rapid development of network technology, increasing three-dimensional scene models is used for fields such as environment navigation, virtual reality, digital city.
A given three-dimensional model is when different angles is observed it, because the visual information that different visual angles has been carried the different directions of this three-dimensional model may show the diverse form of expression.The essence of seeking optimal viewing angle is to find out a viewpoint of carrying maximum fault information, and this viewpoint helps people and more in depth removes to observe and understand given three-dimensional model.In recent years, the optimal viewing angle problem has obtained the broad research of academia, and is applied in a lot of practical problemss, like shape recognition and classification, three-dimensional model view editor, based on the playing up of image, three-dimensional model search etc.
For which type of visual angle is this problem of optimal viewing angle, goes back neither one authority's definition now.When research optimal viewing angle problem, people go to define based on the own practical application of being faced usually.Through research computer graphical psychology; People such as Blanz have proposed four attributes of decision optimal viewing angle: be beneficial to identification, familiarity, can be with function representation and aesthetical standard; And optimal viewing angle receives the geometrical property of three-dimensional model to influence (Blanz, V, etal. to a great extent; What object attributes determine canonical views? PERCEPTION-LONDON-, 1999.28:p.575-600.).In conjunction with these achievements in research, optimal viewing angle often is defined as the visual angle that the maximum visual informations of this model can be provided for people.Wherein, visual information can further show as descriptors such as curvature, topology or profile entropy, and optimal viewing angle is exactly to make these descriptors visible in given angular field of view as much as possible.
Traditional method of finding the solution the three-dimensional model optimal viewing angle mainly contains: based on (G.D.Birkhoff, Mathematics of aesthetics, The world of mathematics (1956) such as traditional aesthetical standard such as golden sections; Pp.2185--2195.), information descriptor of definition, the viewpoint of this information descriptor of definition maximization is best viewpoint (Page, D.L.and Koschan then; A.F.and Sukumar, S.R.and Roui-Abidi, B.and Abidi; M.A, Shape analysis algorithm based on information theory, in Proc.of Proceedings of International Conference on Image Processing (2003); Pp.229--232. etc.), based on optimal viewing angle acquisition methods (Denton, T.and Demirci, the M.F.and Abrahamson of semanteme; J.and Shokoufandeh; A.and Dickinson, S, Selecting Canonical Views for View-Based 3-D Object Recognition; In Proc.of International Conference on Pattern Recognition (2004), pp.273--276. etc.).
Yet; Three-dimensional model only comprises single object usually; And three-dimensional scenic includes a large amount of objects usually, and demonstrates different shapes and material, and this just makes the method for traditional optimal viewing angle that is directed against three-dimensional model can not be transplanted to the optimal viewing angle of three-dimensional scenic well.For three-dimensional scenic, its optimal viewing angle should satisfy following two conditions: can see the object in the scene as much as possible; Make visible object visual effect reach best as far as possible.
Summary of the invention
The object of the present invention is to provide a kind of automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information.
For achieving the above object, the technical scheme that the present invention adopts is:
1), confirms that its direction that makes progress is a positive dirction, and this three-dimensional scenic is carried out normalization with regard to the positive dirction of confirming to given three-dimensional scenic;
2), face viewpoint at the episphere of unit ball and carry out uniform sampling according to the three-dimensional scenic after the normalization;
3) obtain the curvature on each summit, three-dimensional scenic surface, in order to characterize the geometric properties on three-dimensional scenic surface;
4) ask for the surperficial characteristic area of three-dimensional scenic based on the method for three-dimensional scenic surface vertices curvature cluster, and the curvature value that adopts each characteristic area cluster centre is as this regional eigenwert, in order to characterize this regional significance level;
5) based on principal component analysis (PCA) (PCA) method each characteristic area interior location relation of the three-dimensional scenic obtained is analyzed, is obtained the principal direction of each characteristic area, in order to weigh step 2) viewpoint of respectively sampling is to the visual quality of this characteristic area;
6) to see that maximum characteristic areas is a core with obtaining optimum visual quality, propose energy function, and ask for the optimal viewing angle of optimizing energy function based on the method for statistics based on characteristic area eigenwert and the visual quality of characteristic area.
Its concrete execution in step is following:
Step 1: the three-dimensional scenic S for given, confirm the direction that it makes progress, and this three-dimensional scenic is carried out normalization with regard to the positive dirction of confirming, obtain the three-dimensional scenic M after the normalization;
Step 2: to the three-dimensional scenic M after the normalization, at the enterprising capable uniform sampling of first sphere of unit ball, viewpoint collection V obtains sampling;
Step 3: mean curvature is asked on each summit to the three-dimensional scenic M after the normalization, obtains the curvature chart M of scene M
c
Step 4: to curvature chart M
cIn each summit p
i∈ M
c, define one 7 dimensional feature vector e (p
i) remove to describe its geometric properties:
[formula one]
Wherein, (x
i, y
i, z
i) expression summit p
iCoordinate,
Expression summit p
iNormal vector, k
iExpression summit p
iThe curvature value at place;
With the clustering algorithm of average drifting to proper vector e (p
i) do cluster, with curvature chart M
cBe divided into series of features zone M
c={ C
1, C
2..., C
N, to characteristic area set M
cIn each characteristic area C
iWith its cluster centre c
iRepresent the geometric properties that this is regional;
Step 5: to each characteristic area C
i, obtain all summit p of this intra-zone with PCA (PCA)
j∈ C
iCoordinate { x
j, y
j, z
jThree proper vector { t
j, s
j, r
j;
Step 6: for the sampling viewpoint v that obtains in the step 2
i∈ V utilizes the characteristic area M that tries to achieve in the step 4
c={ C
1, C
2..., C
NAnd corresponding cluster centre { c
1, c
2..., c
N∈ C, obtain visualization feature information energy function VN (v
i):
[formula two]
Wherein, k
jExpression cluster centre c
jCurvature value, N is total characteristic area number, target function δ
jDefine as follows:
[formula three]
Step 7: for the sampling viewpoint v that obtains in the step 2
i∈ V utilizes step 5 to each characteristic area C
iProper vector { the t that tries to achieve
j, s
j, r
j, obtain the visual mass-energy function VF (v of three-dimensional scenic M
i):
[formula four]
Wherein, { c
1, c
2..., c
N∈ C is the cluster centre of each characteristic area;
Step 8: utilize the result of step 6 and step 7, obtain each sampling viewpoint v
iThe energy function f of ∈ V
i:
[formula five]
f
i=(VN(v
i)-ω·VF(v
i)
VN (v wherein
i) and VF (v
i) be respectively the visualization feature information energy function and the visual mass-energy function of trying to achieve in step 6 and the step 7, ω is the weights of visual mass-energy function;
Step 9: combine respectively to sample viewpoint v
iThe coordinate of ∈ V and its corresponding energy function define one 4 dimensional feature vector
In order to describe the visual information of this viewpoint:
[formula six]
Wherein, (x
i, y
i, z
i) be sampling viewpoint v
iCoordinate, f
iThe sampling viewpoint v that tries to achieve for step 8
iCorresponding energy function;
With the clustering algorithm of average drifting to proper vector
Do cluster, obtain a series of cluster centre VC={VC
1, VC
2..., VC
K;
Step 10: the cluster centre VC={VC that traversal step nine is tried to achieve
1, VC
2..., VC
K, relatively their energy function item defines optimal viewing angle
For:
[formula seven]
Wherein, f
iExpression cluster centre VC
iThe energy function item of ∈ VC;
Step 11: the optimal viewing angle
of trying to achieve according to step 10 utilizes its coordinate information that three-dimensional scenic is done parallel projection, obtains the perspective view of optimal viewing angle.
The present invention at first, a given three-dimensional scene models need to be confirmed its direction (Fu that makes progress; Hongbo and Cohen-Or, Daniel and Dror, Gideon and Sheffer; Alla; Upright orientation of man-made objects, ACM Transactions on Graphics (2008), pp.42:1--42:7.).According to the positive dirction of confirming, the input scene is carried out normalization.Then, at the unit ball episphere viewpoint is carried out uniform sampling (Polonsky, O.and Patan é; G and Biasotti, S.and Gotsman, C.and Spagnuolo; M, What ' s in an Image? The Visual Computer (2005), PP.840--847.).
The present invention judges that the core rule of best viewpoint is to obtain maximum visual information.In practical study, the researchist can artificially stipulate the visual information descriptor of three-dimensional model usually, like curvature, topology or profile entropy etc., in order to the geometrical property on characterization model surface.The three-dimensional model feature descriptor that the present invention takes is the mean curvature value on each summit of model.Be different from the single object of three-dimensional model, the simple structure of homogenous material, three-dimensional scenic contains usually and surpasses an object and a kind of material.Therefore, the complexity of three-dimensional scenic is much larger than simple three-dimensional model, and the conventional visual information method based on the maximization three-dimensional model is absorbed in the local optimum predicament when being applied on the three-dimensional scenic easily, can not finely satisfy three-dimensional scenic optimal viewing angle problem.Therefore; Be applied to average drifting clustering method (Comaniciu among the present invention; D.and Meer, P, Mean shift:A robust approach toward feature space analysis; IEEE Transactions on Pattern Analysis and Machine Intelligence (2002), pp.603--619.).Through cluster, can whole three-dimensional scenic be divided into a series of characteristic area.Maximization visual information method based on characteristic area is found the solution the optimal viewing angle that obtains; Can fully take into account the interested position of each people in the whole three-dimensional scenic; Weigh of the contribution of each zone of whole three-dimensional scenic to visual information; And need not emphasis be confined to certain outstanding especially position, thereby the predicament of jumping out local optimum smoothly.
Simultaneously, the present invention has not only considered maximum visual information, also on the basis that obtains maximum visual information, and the visual quality of each characteristic area of optimization.For each characteristic area that marks off, utilize principal component analytical method (PCA) that all summits in the zone are analyzed, and obtain its three principal directions.For each characteristic area, we are the best line-of-sight direction with the direction vertical with its principal direction.Therefore, only need to calculate the inner product of sight line and principal direction, just can assess out the visual effect quality of certain viewpoint this characteristic area.
Take all factors into consideration two factors of maximum visual information and the best visual effect, rationally arrange the weights of the two, construct the evaluation good and bad energy function of viewpoint of respectively sampling.Then, utilize the average drifting clustering algorithm that each sampling viewpoint is carried out cluster once more, obtain the cluster centre of a series of viewpoints.At last, the method for utilization statistics sorts to the energy function of each viewpoint cluster centre, chooses the maximum cluster centre of gained energy function as best viewpoint position, and utilizes parallel projection to obtain the perspective view of optimal viewing angle.
Description of drawings
Fig. 1 is the process flow diagram that the present invention is based on the automatic acquisition methods of three-dimensional scenic optimal viewing angle of maximization visual information;
Fig. 2 is a synoptic diagram of the present invention;
Fig. 3 shows that different visual angles embodies the different information of three-dimensional scenic, provides the traditional defective of optimal viewing angle method for solving on the three-dimensional scenic problem to three-dimensional model simultaneously;
Fig. 4 shows the cluster centre of each characteristic area and three principal directions that each characteristic area principal component analysis (PCA) (PCA) is tried to achieve;
Fig. 5 shows the sample cluster centre of viewpoint and choosing of optimal viewing angle, analyzes the convergent tendency that increase of optimal viewing angle with sampling viewpoint number simultaneously;
Fig. 6 shows the partial 3-D scene optimal viewing angle perspective view (the 1st row) that the inventive method is tried to achieve, and is given in the characteristic area cluster centre (the 2nd row) of this three-dimensional model under this visual angle and the comparing result (the 3rd, 4 row) of two kinds of traditional optimal viewing angle methods that are directed against three-dimensional model simultaneously.
Embodiment
To be elaborated to the present invention according to accompanying drawing below.
Fig. 1 is a process flow diagram of the present invention.The present invention mainly is divided into 11 steps:
Referring to Fig. 1,2:
Step 1: the three-dimensional scenic S for given, confirm the direction that it makes progress, be defined as positive dirction.And this three-dimensional scenic is carried out normalization with regard to the positive dirction of confirming, obtain the three-dimensional scenic M after the normalization;
Step 2: to the three-dimensional scenic M after the normalization, on the episphere of the unit ball that surrounds M, carry out uniform sampling, viewpoint collection V obtains sampling.For different sampling viewpoints, because it has carried the geometric properties information of the different directions of three-dimensional scenic M, its visual effect may demonstrate the distinct form of expression (Fig. 3).
Step 3: for a given three-dimensional model, its surperficial geometric properties information spinner will be presented as the curvature characteristic between each summit, surface.Therefore,, obtain the mean curvature on each summit, obtain the curvature chart M of scene M the three-dimensional scenic M after the normalization
c
Step 4: the curvature chart M that obtains scene M
cAfter, need provide mathematical description of a quantification to each summit in the curvature chart.For each summit p in the curvature chart
i∈ M
c, define one 7 dimensional vector e (p
i) remove to describe its geometric properties:
[formula one]
Wherein, (x
i, y
i, z
i) expression summit p
iCoordinate,
Expression summit p
iNormal vector, k
iExpression summit p
iThe curvature value at place.Vector e (p
i) comprised the geometric position information and the surface characteristics information on current summit simultaneously.
Traditional optimal viewing angle algorithm that is directed against three-dimensional model is like (V á zquez; P.P.and Feixas; M.and Sbert, M.and Llobet, A; Viewpoint entropy:a new tool for obtaining good views of molecules; In Proc.of Proceedings of the Symposium on Data Visualisation 2002 (2002), pp.183--188.) grade is absorbed in local optimum predicament (like Fig. 3) easily, therefore is inappropriate for the best problem of three-dimensional scenic that solves.The present invention with the average drifting clustering algorithm to proper vector e (p
i) do cluster, cluster result is with curvature chart M
cBe divided into series of features zone M
c={ C
1, C
2..., C
N.To each characteristic area C
i, with its cluster centre c
iRepresent the geometric properties that this is regional, some c
iVector expression be:
[formula two]
Wherein, (x
i, y
i, z
i) the cluster centre c that calculates of expression
iCoordinate,
The cluster centre c that expression is calculated
iNormal vector, k
iThe cluster centre c that expression is calculated
iThe curvature value at place.
Step 5: to each characteristic area C
i, with PCA (PCA) to all summit p of this intra-zone
j∈ C
iCoordinate { x
j, y
j, z
jAnalyze, and obtain its three proper vector { t
j, s
j, r
j(Fig. 4).Wherein, this summit, zone is distributed that play a decisive role is vectorial t
j(three eigenwerts supposing it are arranged as λ
1>=λ
2>=λ
3).
Step 6: for the sampling viewpoint v that obtains in the step 2
i∈ V utilizes the characteristic area M that tries to achieve in the step 4
c={ C
1, C
2..., C
NAnd corresponding cluster centre { c
1, c
2..., c
N∈ C, the some c that defines in the formula two utilized
iVector expression e (c
i), obtain visualization feature information energy function VN (v
i):
[formula three]
Wherein, k
jExpression cluster centre c
jCurvature value, N is total characteristic area number, target function δ
jDefine as follows:
[formula four]
Wherein, current view point v
iCan see cluster centre c
jBy determine type D (v
i, c
j) decision:
[formula five]
Wherein, determine type D (v
i, c
j) be defined as:
[formula six]
D(v
i,c
j)=(p(v
i)-p(c
j)).(n(c
j))
In the formula, the coordinate of p (.) expression point, n (.) representes the normal vector of this point.
Step 7: for the sampling viewpoint v that obtains in the step 2
i∈ V utilizes step 5 to each characteristic area C
iProper vector { the t that tries to achieve
j, s
j, r
j, obtain the visual mass-energy function VF (v of three-dimensional scenic M
i):
[formula seven]
Wherein, { c
1, c
2..., c
N∈ C is the cluster centre of each characteristic area.Can know by step 5, because t
jBe vector that this summit, zone is distributed and plays a decisive role, during to this regional visual quality, only need satisfy sight line and vectorial t in the calculating sampling viewpoint
jVertically just can obtain good effect (referring to Fig. 5).
Step 8: utilize the result of step 6 and step 7, obtain each sampling viewpoint v
iThe energy function f of ∈ V
i:
[formula eight]
f
i=(VN(v
i)-ω·VF(v
i)
VN (v wherein
i) and VF (v
i) be respectively the visualization feature information energy function and the visual mass-energy function of trying to achieve in step 6 and the step 7, ω is the weights of visual mass-energy function.Energy function f
iBig more, just represent this viewpoint to meet people's visual demand more.
Step 9: combine respectively to sample viewpoint v
iThe coordinate of ∈ V and its corresponding energy function define one 4 dimensional vector
In order to describe the visual information of this viewpoint:
[formula nine]
Wherein, (x
i, y
i, z
i) be sampling viewpoint v
iCoordinate, f
iThe sampling viewpoint v that tries to achieve for step 8
iCorresponding energy function.
With reference to step 4, with the average drifting clustering algorithm to proper vector
Do cluster, obtain a series of cluster centre VC={VC
1, VC
2..., VC
K.For each cluster centre VC
i, can define one 4 dimensional vector α (VC
i):
[formula ten]
α(VC
i)=(x
i,y
i,z
i,f
i)
In the formula, (x
i, y
i, z
i) be the coordinate of sampling viewpoint cluster centre, f
iEnergy function value for the sampling viewpoint cluster centre of trying to achieve.
Step 10: the cluster centre VC={VC that traversal step nine is tried to achieve
1, VC
2..., VC
K, utilize the vectorial α (VC that describes each cluster centre visual characteristic
i), compare their energy function item f
i, the definition optimal viewing angle
For:
[formula 11]
Wherein, f
iExpression cluster centre proper vector α (VC
i) the energy function item.
Step 11: the optimal viewing angle
of trying to achieve according to step 10 utilizes its coordinate information that three-dimensional scenic is done parallel projection, obtains the perspective view of optimal viewing angle.(Fig. 6)
As stated, the present invention proposes a kind of automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information.The curvature of utilizing each summit of three-dimensional scenic is in order to characterizing the surface geometry characteristic of this scene, and utilizes clustering method to obtain the characteristic area of this three-dimensional scenic.For each sampling viewpoint, assess its visual properties total amount with and to the visual effect of each characteristic area, finally find out the optimal viewing angle of this three-dimensional scenic based on clustering algorithm and statistical method.This method makes full use of the geometric properties information on given three-dimensional scenic surface, when obtaining the maximum visual quantity of information, takes into account the visual quality of each characteristic area, and the optimal viewing angle of obtaining fully caters to people's visual demand.
Although with reference to accompanying drawing the present invention is explained and describe, the professional and technical personnel should be appreciated that, without departing from the spirit and scope of the present invention, can carry out various other changes, additions and deletions therein or to it.
Claims (6)
- One kind based on the maximization visual information the automatic acquisition methods of three-dimensional scenic optimal viewing angle, it is characterized in that comprising following steps:1), confirms that its direction that makes progress is a positive dirction, and this three-dimensional scenic is carried out normalization with regard to the positive dirction of confirming to given three-dimensional scenic;2), face viewpoint at the episphere of unit ball and carry out uniform sampling according to the three-dimensional scenic after the normalization;3) obtain the curvature on each summit, three-dimensional scenic surface after the normalization, in order to characterize the geometric properties on three-dimensional scenic surface;4) ask for the surperficial characteristic area of three-dimensional scenic based on the method for three-dimensional scenic surface vertices curvature cluster, and the curvature value that adopts each characteristic area cluster centre is as this regional eigenwert, in order to characterize this regional significance level;5) based on principal component analysis (PCA) (PCA) method each characteristic area interior location relation of the three-dimensional scenic obtained is analyzed, is obtained the proper vector of each characteristic area, in order to weigh step 2) viewpoint of respectively sampling is to the visual quality of this characteristic area;6) to see that maximum characteristic areas is a core with obtaining optimum visual quality; Proposition is based on the energy function of characteristic area eigenwert and the visual quality of characteristic area; And ask for the optimal viewing angle of optimizing energy function based on the method for statistics, concrete execution in step is following:The first step: for step 2) the sampling viewpoint v that obtains in i∈ V utilizes the characteristic area { C that tries to achieve in the step 4) 1, C 2..., C N∈ M cCluster centre { c with correspondence 1, c 2..., c N∈ C, wherein M cBe defined as the curvature chart of three-dimensional scenic, obtain visualization feature information energy function VN (v i):[formula one]Wherein, κ jExpression cluster centre c jCurvature value, N is total characteristic area number, target function δ jDefine as follows:[formula two]Second step: for step 2) the sampling viewpoint v that obtains in i∈ V utilizes step 5) to each characteristic area C iProper vector { the t that tries to achieve j, s j, r j, obtain the visual mass-energy function VF (v of three-dimensional scenic M i):[formula three]Wherein, { c 1, c 2..., c N∈ C is the cluster centre of each characteristic area;The 3rd step: utilize the first step and the result in second step, obtain each sampling viewpoint v iThe energy function f of ∈ V i:[formula four]f i=VN(v i)-ω·VF(v i)VN (v wherein i) and VF (v i) be respectively the visualization feature information energy function and the visual mass-energy function of trying to achieve during the first step and second goes on foot, ω is the weights of visual mass-energy function;The 4th step: combine respectively to sample viewpoint v iThe coordinate of ∈ V and its corresponding energy function define one 4 dimensional feature vector In order to describe the visual information of this viewpoint:[formula five]Wherein, (x i, y i, z i) be sampling viewpoint v iCoordinate, f iBe the sampling viewpoint v that tried to achieve in the 3rd step iCorresponding energy function;With the clustering algorithm of average drifting to proper vector Do cluster, obtain a series of cluster centre VC={VC 1, VC 2..., VC K;The 5th step: travel through the 4th and go on foot the cluster centre VC={VC that tries to achieve 1, VC 2..., VC K, relatively their energy function item defines optimal viewing angle For:[formula six]Wherein, f iExpression cluster centre VC iThe energy function item of ∈ VC;
- 2. the automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information as claimed in claim 1; It is characterized in that; The concrete execution in step of described step 1) is: for given three-dimensional scenic S; Confirm that its direction that makes progress is a positive dirction, and this three-dimensional scenic is carried out normalization, obtain the three-dimensional scenic M after the normalization with regard to the positive dirction of confirming.
- 3. the automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information as claimed in claim 1; It is characterized in that; Described step 2) concrete execution in step is: to the three-dimensional scenic M after the normalization; At the enterprising capable uniform sampling of first sphere of unit ball, viewpoint collection V obtains sampling.
- 4. the automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information as claimed in claim 1; It is characterized in that; The concrete execution in step of described step 3) is: mean curvature is asked on each summit to the three-dimensional scenic M after the normalization, obtains the curvature chart M of scene M c
- 5. the automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information as claimed in claim 1 is characterized in that the concrete execution in step of described step 4) is: to curvature chart M cIn each summit p i∈ M c, define one 7 dimensional feature vector e (p i) remove to describe its geometric properties:[formula seven]Wherein, (x i, y i, z i) expression summit p iCoordinate, Expression summit p iNormal vector, κ iExpression summit p iThe curvature value at place;With the clustering algorithm of average drifting to proper vector e (p i) do cluster, with curvature chart M cBe divided into series of features zone { C 1, C 2..., C N∈ M c, to M cIn each characteristic area C iWith its cluster centre c iRepresent the geometric properties that this is regional.
- 6. the automatic acquisition methods of three-dimensional scenic optimal viewing angle based on the maximization visual information as claimed in claim 1 is characterized in that the concrete execution in step of described step 5) is: to each characteristic area C i, obtain all summit p of this intra-zone with PCA (PCA) j∈ C iCoordinate { x j, y j, z jThree proper vector { t j, s j, r j.
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