CN101017476A - Characteristic analytical method for product point clouds surface based on dynamic access model - Google Patents
Characteristic analytical method for product point clouds surface based on dynamic access model Download PDFInfo
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Abstract
This invention provides one product cloud shape property analysis method based on data dynamic memory module, which comprises the following steps: reading the output data from digital device into memory and establishing linear memory structure for data; then adopting 3D R*-tree for rapid space polymer sorting on data and to establish data dynamic memory; then in the browsing process, using current data point as aim point to get the local reference data set by use of Bezier curve accuracy near to point set to estimate curve rate ; finally mapping the data curve rate as RGB color mode.
Description
Affiliated technical field
The invention provides a kind of product point cloud surface-type feature analytical approach, belong to product reverse Engineering Technology field based on Data Dynamic access model.
Background technology
In reverse-engineering, the analysis of some cloud surface-type feature can be carried out the estimation of profile characteristic parameter to realize the globality assessment of some cloud surface-type feature to all measurement points based on product local profile data, be one of guardian technique in data pre-service and the surface model process of reconstruction, its implementation mainly contains three kinds: tri patch method vector method, local data's point set parabola approach the estimation technique and sampled point 4D Shepard interpolation calculation method.
Tri patch method vector method adopts the triangular surface model that a cloud is carried out interpolation or approaches, and according to the changing condition of adjacent triangular faces method direction vector in the triangular surface model cloud profile is carried out whole profile characteristic area assessment.This method is put cloud comparatively uniformly for DATA DISTRIBUTION, and assessment result is comparatively accurate, but exists triangular surface model generative process complexity, storage redundancy degree height, the low deficiency that waits of adjacent dough sheet search efficiency.In addition, the interference of noise data and cause the distortion of surface-type feature analysis result in the easy receptor site cloud of this method.
Local data's point set parabola approach the estimation technique with any one data point in the cloud as impact point, by inquiring about its neighbor point acquisition point cloud local profile reference data point set, with parabola this data point set is approached, calculate then current on parabola the curvature value of corresponding point, and with the profile curvature estimation result of this value as impact point corresponding point on a cloud profile.Adopt this method can calculate the profile curvature value of a cloud total data point, describe a cloud surface-type feature according to the curvature variation of data points distribution situation, its data adaptability is better than tri patch method vector method.But because this method can't realize effectively that based on the access management of static data structure realization cloud data the data neighbour puts inquiry, its data-handling efficiency is subjected to the restriction of point cloud data scale; In addition, parabolic low for the comparatively complicated local data's point set approximation accuracy of profile, influence point cloud overall data surface-type feature precision of analysis.
Sampled point 4D Shepard interpolation calculation method at first adopts grid space Octree divided method that a cloud is carried out overall sampling processing, a cloud sampled data is set up 4D Shepard interpolation curved surface model, can calculate this curvature value in the coordinate figure substitution 4D Shepard curved surface formula with arbitrfary point in the cloud.Behind the profile curvature value that calculates all data points of cloud, subsequent treatment and local data's point set parabola approach the estimation technique together.This method is put the interference that cloud can be avoided data noise well uniformly for DATA DISTRIBUTION, point cloud curvature is calculated process stabilization, but because currently used some cloud sampling algorithm is poor to the adaptivity of processing data, in cloud data skewness, surface-type feature structure than under the complicated situation, the sampled result accuracy is low, cause the 4D Shepard surface model precision that generates low, thereby influenced final surface-type feature precision of analysis.In addition, in segmentation of grid space Octree and 4D Shepard surface interpolation, all carried out a large amount of parabolas and approached local data's point set processing, reduced the integral body of algorithm and carried out efficient.
In sum, existing some cloud surface-type feature analytical approach only can obtain analysis result preferably under given conditions, cloud data for different pieces of information scale, topological classification and profile complexity lacks versatility, causes the accuracy of a stability of cloud profile parameter calculation procedure and result of calculation all can't satisfy engineering demand.
Summary of the invention
For overcoming the deficiency of existing some cloud surface-type feature analytical approach, the object of the invention is to provide a kind of product point cloud surface-type feature analytical approach based on Data Dynamic access model.This method can be carried out the globality assessment to a cloud surface-type feature exactly, carries out product model according to assessment result and rebuilds, and can significantly improve reverse-engineering modeling efficiency and model accuracy, and its implementation is:
A kind of product point cloud surface-type feature analytical approach based on Data Dynamic access model is characterized in that steps in sequence is: 1) cloud data input; 2) create Data Dynamic access model based on three-dimensional R*-tree for the some cloud; 3) the profile curvature value of all data points in the estimation point cloud; 4) according to the profile curvature value distribution of all data points in the cloud cloud surface-type feature is carried out total evaluation.
For realizing goal of the invention, described multidimensional data dynamic access mechanism, point cloud data file with product digitizer output in step 1) is read in the storer, and sets up the linear list storage organization for data, to satisfy the demand that data this storage of nodal basis and data are traveled through in proper order.
For realizing goal of the invention, described product point cloud surface-type feature analytical approach based on Data Dynamic access model, in step 2) in, with mutually nested multidimensional rectangle cloud data being carried out cluster divides, organize the topological proximity relations between the data point, for a cloud is set up a kind of balanced tree Data Dynamic access model of supporting the data dynamic access.
For realizing goal of the invention, described product point cloud surface-type feature analytical approach based on Data Dynamic access model, in step 3), the cloud data linear list is carried out the order traversal, with current traversal point is that impact point is inquired about its neighboring data point acquisition point cloud local profile reference data point set, and it is approached generate free spline surface, on curved surface, calculate the Gaussian curvature value of impact point parameter corresponding point, with the profile curvature value of this curvature value as impact point.After finishing above-mentioned ergodic process, can calculate all data point profile curvature values.
For realizing goal of the invention, described product point cloud surface-type feature analytical approach based on Data Dynamic access model, the acquisition methods of point cloud local profile reference data is to be the centre of sphere with the impact point in step 3), but set up the hollow ball zone of a self-adaptation to external diffusion, itself and Data Dynamic access model multidimensional rectangle are carried out the zone ask friendship, obtain the neighbour's point that falls into the common factor space.Put the formation point cloud local profile reference data with impact point and neighbour thereof and express the point cloud local profile geometric properties.
For realizing goal of the invention, described product point cloud surface-type feature analytical approach based on Data Dynamic access model, in step 3), in point cloud local profile reference data rectangular domain parameterized procedure, at first need the point cloud local profile reference data that is obtained is adopted little section to approach and point set is projected to little section; Set up the parameter coordinate system for the projection point set then, the projection point set is transformed under this coordinate system by world coordinate system; At last, calculate the two-dimentional minimum area-encasing rectangle territory of point set, in this zone the subpoint coordinate figure is carried out normalization process, the subpoint coordinate after the processing is the parameter value of corresponding point cloud local profile reference data point.
For realizing goal of the invention, described product point cloud surface-type feature analytical approach based on Data Dynamic access model, in step 4), the profile curvature value of all data points in the cloud is mapped as the RGB color value, thereby generate the colored curvature cloud atlas of some cloud profile, with the color region reflection point cloud surface-type feature of curvature cloud atlas gradual change.
The present invention compared with prior art has the following advantages:
1) set up the dynamic access model for the some cloud, divided the topological relation of organizing data, improved and put the adaptivity of cloud surface-type feature analytical approach for the cloud data type by cloud data being carried out space clustering.
2) the local reference data point set of acquisition point cloud profile has improved the stability of some cloud surface-type feature analysis result with accurate performance point cloud local profile feature fast.
3) adopt the Bezier patch that point cloud local profile reference data is accurately approached, improved some cloud surface-type feature precision of analysis.
Description of drawings
Fig. 1 is a program flow diagram of the present invention.
Fig. 2 is the Data Dynamic access model synoptic diagram that step 2 of the present invention is set up.
Fig. 3～Fig. 7 is that car load external type millet cake mysorethorn of the present invention is executed the three-dimensional minimum area-encasing rectangle illustraton of model of each layer index node of routine Data Dynamic access model.
Fig. 8 is that step 3 cloud data profile curvature of the present invention is calculated program flow diagram.
Fig. 9～Figure 11 is a point cloud local profile reference data parametrization synoptic diagram of the present invention.
Figure 12 is the present invention approaches generation to certain point cloud local profile reference data a Bezier patch.
Figure 13～Figure 14 is that surface-type feature analytical approach of the present invention is to certain body of a motor car cloud data surface-type feature analytical effect.
Figure 15～Figure 16 is that surface-type feature analytical approach of the present invention is to Micky Mouse cloud data surface-type feature analytical effect.
Embodiment
The invention will be further described below in conjunction with accompanying drawing.
As shown in Figure 1, be that the present invention puts cloud surface-type feature routine analyzer realization flow figure.Some cloud surface-type feature routine analyzer based on Data Dynamic access model comprises data entry program 1, cloud data dynamic access model construction program 2, cloud data profile curvature calculation procedure 3, and some cloud surface-type feature globality appraisal procedure 4.Wherein, data entry program 1 is responsible for reading in the three dimensional point cloud of product digital collection equipment output, and creates the linear list storage organization for it, with support the data point linear precedence is traveled through.When the shared stored of a cloud linear list space exceeds internal storage capacity, can be stored in auxilliary depositing in the data file of equipment.Cloud data dynamic access model construction program 2 adopts nested three-dimensional rectangle that cloud data is carried out the dynamic space cluster and divides, for the data linear list that data entry program 1 is generated is set up upper strata R*-tree dynamic space index structure.Cloud data profile curvature calculation procedure 3 travels through the cloud data linear list by order, calculates the profile curvature value of all data points in the cloud.In a cloud surface-type feature globality appraisal procedure 4, cloud data is put the profile curvature value be mapped as RGB color cloud atlas, with the color region reflection point cloud surface-type feature of curvature cloud atlas gradual change.
As shown in Figure 2, be that cloud data dynamic access model construction program 2 employing C++ programming languages of the present invention are the Data Dynamic access model synoptic diagram that three-dimensional R*-tree sets up for the some cloud.The index node of Data Dynamic access model is divided into root node, inner index node and leaf index node, stored the three-dimensional minimum area-encasing rectangle parameter (being three-dimensional rectangle two diagonal angle vertex information) of his father's node address, child node (may be inner index node, also may be the leaf index node) address table and this node in the wherein inner index node.Stored minimum area-encasing rectangle (the Spatial Minimum Box Rectangle SMBR) information in space of his father's node address, data node address table and this node in the leaf index node.The root node structure of multidimensional data dynamic access model data structure may be identical with inner node, also may be identical with the leaf index node, and difference is that the father node address of its root node is sky.The address of the data node storage space object information item in the multidimensional data dynamic access model data structure.In the realization of all kinds of index nodes, the present invention utilizes C++ pure virtual function technology, in program run, differentiates the node type according to the rreturn value of Virtual Function.For upper limit M, the lower limit m of the child node number of each layer of multidimensional data dynamic access model node, and the R* tree node inserts the value of number R again, all is provided with voluntarily according to the scale of point cloud data by the user, gets m=M * 40% usually, R=M * 30%.
Shown in Fig. 3～7, be that 2 pairs of point of invocation cloud Data Dynamic access model construction programs of the present invention come from the three-dimensional minimum area-encasing rectangle illustraton of model of each layer of Data Dynamic access model data structure node that the car load outer profile point cloud data of Atos triplex scanner output is set up.The number of data points of testing used some cloud is 85.6112 ten thousand, and the indexing parameter m=40, the M=100 that are adopted insert nodal point number R=30 again, and the multidimensional data dynamic access model data structure construction time is about 110 seconds.Wherein Fig. 3 has shown the SMBR of the root node of car load outer profile Data Dynamic access model data structure, Fig. 4 has shown the SMBR of the inner index node of its first floor, Fig. 5) shown the SMBR of the inner index node of the second layer, Fig. 6 has shown leaf index node SMBR, Fig. 7 has shown data node layer, be original car load outer profile data, owing to be symmetrical part, so only scanned a side of vehicle body.This experiment shows, adopts the Three-Dimensional Dynamic index structure can accurately realize the space clustering of extensive scattered data being in the product reverse engineering is divided, and has the higher data adaptivity.
As shown in Figure 8, be cloud data profile curvature calculation procedure 3 realization flow figure of the present invention.Cloud data profile curvature calculation procedure 3 is in traveling through in proper order the cloud data linear list, with the current data point that traverses as impact point, invocation target point neighbour polling routine 5, data point set parametric program 6, Bezier patch approach program 7 and impact point curvature estimation program 8 successively, the profile curvature value of all data points in the last output point cloud.Wherein the k neighbour of 5 pairs of impact points of impact point neighbour polling routine carries out fast query, accurately acquisition point cloud local profile reference data point set.The data point set that 6 couples of impact point neighbours of data point set parametric program polling routine 3 is extracted carries out rectangular domain free form surface parameter and distributes, for put concentrate each data point distribute a two-dimensional parameter coordinate (u, v).The Bezier curved surface approaches point cloud local profile reference data point set after 7 pairs of parametrizations of program and adopts least square method accurately to approach and generate and be used to embody a Bezier patch of cloud profile infinitesimal geometry character.Impact point curvature estimation program 8 is the parameter value that impact point distributed according to data point set parametric program 6, approaches the Gaussian curvature value that calculates corresponding point on the curved surface that generates in the program 7 at the Bezier curved surface, and with the profile curvature value of this value as impact point.
Impact point neighbour polling routine 5 comprises a polling routine 9, range query program 10.Wherein, some polling routine 9 is responsible for determining fast the leaf index node address at impact point place, and 10 of range query programs are responsible for the data point that search falls into user designated area.Impact point neighbour polling routine 5 is the leaf index node at point of invocation polling routine 9 definite impact point place in Data Dynamic access model at first, by three-dimensional minimum area-encasing rectangle parameter initialization hollow ball region S (c, r of this leaf index node
1, r
2), its centre of sphere c is three-dimensional minimum area-encasing rectangle center, internal diameter r
1=0, external diameter
(r is three-dimensional minimum area-encasing rectangle circumsphere radius, and k counts for the impact point neighbour of user's appointment); Call 10 search of range query program then and fall into S (c, r
1, r
2) data point, these data points are the neighbor point of impact point.If the vicinity of being obtained is counted less than k, then to S (c, r
1, r
2) according to formula r
1=r
2,
Carry out the self-adaptation diffusion; If counting, the vicinity of being obtained specifies the k value, k data point information before then program is returned more than or equal to the user.
Point polling routine 9 specific implementation steps are as follows: 1) establish pointer pCurrent and point to multidimensional data dynamic access model root node; 2) if pCurrent indication node type is the leaf index node, and the query aim point falls into this node SMBR, and then program is returned pCurrent; If query aim point does not fall into this node SMBR, then program is returned null value; 3) order travels through the child node of pCurNode indication node, in traversal, and recursive call point query manipulation, newly-built pointer type retPtr, the rreturn value of receiving station query manipulation; 4) return retPtr.
Range query program 10 specific implementation steps are as follows: 1) establish pCurNode and point to multidimensional data dynamic access model data structure root node; 2) if pCurNode indication node is three-dimensional minimum area-encasing rectangle of leaf node type and this node and space querying territory to intersect, the child node that then pCurNode indication inter-node is fallen into the space querying territory adds overall linear list successively to, and program is returned then; 3) order travels through the child node of pCurNode indication node, in traversal, operates as query sub tree recursive call range query with current child node.
Shown in Fig. 9～11, be the course of work synoptic diagram of data point set parametric program 6 of the present invention.In Fig. 9, impact point is x, and the point cloud local profile reference data point set is X.Adopt least square method to generate little section of point set X, then with X to the projection of little section, obtain projection point set X '.In Figure 10, be true origin with the subpoint x ' of the middle impact point x of X ', apart from its farthest subpoint constitute vectorial u with it in that point set X ' is middle with x ', the normal vector that other establishes little section is w, can get vector v=w * u.Set up right hand orthogonal coordinate system C by vectorial u, v, w.As shown in figure 11, point set X ' is transformed under this coordinate system, gets point set X ", " the u coordinate figure and the v coordinate figure sort ascending of middle data point get two coordinate components sequence U respectively to X
c={ u
Ci| i=0,1 ..., k} and V
c={ V
Ci| i=0,1 ..., k}.Under coordinate system C, calculate point set X " minimum the area-encasing rectangle ((u in uv plane
C0, v
C0), (u
Ck, v
Ck)) and u to length of side l
uWith v to length of side l
v, " middle arbitrary data point (u then to point set X
i, v
i) coordinate by formula:
Carry out normalization process.Because point set X, X ' and X " in data point have mapping relations one by one, gained (u
i', v
i') be parameter value to corresponding three-dimensional data point among the point set X.
Approach in the realization of program 7 at the Bezier curved surface, establish to the point cloud local profile reference data point set X after the parametrization n * m time Bezier surface interpolation formula is
(q
t∈X，t＝0，1，2，…，k)。If the Bezier curved surface basis function in the interpolation formula is designated as D
I, j(u, v)=B
J, n(u) B
J, m(v), then Bezier surface interpolation formula can be write as matrix equation:
To this equation, point cloud local profile reference data point is concentrated count k may be greater than ((n * m), therefore the control point set of corresponding interpolation curved surface has infinitely and separates more n * m), also may be less than or equal to, and also may not have separating or unique solution is arranged.The present invention adopts singular value decomposition method that this matrix equation is found the solution, and finally can get an optimum solution of the control point set of interpolation curved surface.The svd result of matrix number D of setting up departments is: D=UWV
T, wherein U is the row orthogonal matrix of (k+1) * (m+1) (n+1), and W is (k+1) rank diagonal matrix that element is positive number or 0, and V is (m+1) (n+1) rank orthogonal matrix.Thereby the Bezier curved surface
The control point set is finally separated and can be written as:
After obtaining control the separating of point set, the Bezier curved surface expression formula that can determine to approach the point cloud local profile reference data point set.
As shown in figure 12, approach the curved surface of generation for the Bezier curved surface approaches in 5 pairs of points of program cloud the comparatively complicated reference data of one group of local profile structure, the data approximation accuracy is 0.0031mm (curved surface and reference data is apart from average), and standard deviation is 0.0027.Experiment shows, adopts the Bezier curved surface can not be subjected to the restriction of data profile complicacy.
In impact point curvature estimation program 6, the computing formula of the Gaussian curvature value of the corresponding parameter position of impact point is on the curved surface:
Bezier curved surface S in the formula (u v) goes up the normal vector of optional position, can by
Try to achieve.S in addition
u, S
vBe the single order local derviation of n * m Bezier curved surface, S
Uu, S
Vv, S
UvSecond order local derviation for curved surface.At last with the profile curvature value of trying to achieve Gaussian curvature value as impact point.
In a cloud surface-type feature globality appraisal procedure 4, data type curvature of face value in the cloud is sorted, add up its distribution range, obtain curvature value distributed area [C
a, C
b] and the curvature average
RGB color index table in the computing machine is divided into increases 1 or subtract 1 stepping interval [rgb (0,255,0), rgb (255,255,0)] and [rgb (255,255,0), rgb (255,0,0)] two color index intervals, expressed color progressive relationship is to be gone forward one by one to yellow by green, is gone forward one by one to redness by yellow again.In cloud data profile curvature interval [
] with [rgb (0,255,0), rgb (255,255,0)] between be mapping relations such as segment such as branch such as its foundation grade; To interval [
] do to handle equally with [rgb (255,255,0), rgb (255,0,0)].Can be implemented in cloud data profile curvature lower region at last and show, show with yellow system,, show with the highlighted territory of redness at the maximum near zone of curvature at cloud data profile curvature average near zone with green system.
As shown in figure 13, be certain body of a motor car cloud data, Figure 14 is that its surface-type feature is analyzed RGB color cloud atlas.Figure 15 for the present invention to the complicated Micky Mouse cloud data of profile, Figure 16 is that its surface-type feature is analyzed RGB color cloud atlas.Cloud data profile curvature interval from low to high is patterned into by dark to bright color region among the figure.Usually the zone that profile curvature is less and amplitude of variation is low is a model main structure profile, in curved surface modeling, this part area relative data extract is come out, and finishes the structure of the main structure of product profile fast.Among the figure, the transitional region of main structure profile is a Fillet Feature, and this part feature is shown as the higher Huang of brightness, red colour system because profile curvature is big and amplitude of variation is also big.
Claims (6)
1, a kind of product point cloud surface-type feature analytical approach based on Data Dynamic access model, it is characterized in that comprising successively following steps: 1) the point cloud data file with product digitizer output is read in the storer, and sets up the linear list storage organization for data; 2) create Data Dynamic access model based on three-dimensional R*-tree for the some cloud; 3) the profile curvature value of all data points in the estimation point cloud; 4) according to the profile curvature value distribution of all data points in the cloud cloud surface-type feature is carried out total evaluation.
2, the product point cloud surface-type feature analytical approach based on Data Dynamic access model as claimed in claim 1, it is characterized in that: step 2) in, with mutually nested multidimensional rectangle cloud data being carried out cluster divides, organize the topological proximity relations between the data point, for a cloud is set up a kind of balanced tree Data Dynamic access model of supporting the data dynamic access.
3, the product point cloud surface-type feature analytical approach based on Data Dynamic access model as claimed in claim 1, it is characterized in that: in the step 3) cloud data linear list is carried out the order traversal, with current traversal point is that impact point is inquired about its neighboring data point acquisition point cloud local profile reference data point set, and it is approached generate free spline surface, on curved surface, calculate the Gaussian curvature value of impact point parameter corresponding point, with the profile curvature value of this curvature value as impact point, traversal can calculate all data point profile curvature values after finishing.
4, the product point cloud surface-type feature analytical approach based on Data Dynamic access model as claimed in claim 3, it is characterized in that: the acquisition methods of point cloud local profile reference data point set is to be the centre of sphere with the impact point, but set up the hollow ball zone of a self-adaptation to external diffusion, itself and Data Dynamic access model multidimensional rectangle are carried out the zone ask friendship, obtain the neighbour's point that falls into the common factor space, put the formation point cloud local profile reference data with impact point and neighbour thereof.
5, the product point cloud surface-type feature analytical approach based on Data Dynamic access model as claimed in claim 3, it is characterized in that: in point cloud local profile reference data rectangular domain parameterized procedure, at first need the point cloud local profile reference data that is obtained is adopted little section to approach and point set is projected to little section; Set up the parameter coordinate system for the projection point set then, the projection point set is transformed under this coordinate system by world coordinate system; At last, calculate the two-dimentional minimum area-encasing rectangle territory of point set, in this zone the subpoint coordinate figure is carried out normalization process, the subpoint coordinate after the processing is the parameter value of corresponding point cloud local profile reference data point.
6, the product point cloud surface-type feature analytical approach based on Data Dynamic access model as claimed in claim 1, it is characterized in that: in the step 4), the profile curvature value of all data points in the cloud is mapped as the RGB color value, thereby generate the colored curvature cloud atlas of some cloud profile, with the color region reflection point cloud surface-type feature of curvature cloud atlas gradual change.
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