CN106373186A - 3D model topology mapping method based on deformation driving - Google Patents

3D model topology mapping method based on deformation driving Download PDF

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CN106373186A
CN106373186A CN201610782774.3A CN201610782774A CN106373186A CN 106373186 A CN106373186 A CN 106373186A CN 201610782774 A CN201610782774 A CN 201610782774A CN 106373186 A CN106373186 A CN 106373186A
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CN106373186B (en
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盛斌
马菁
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Shanghai Jiaotong University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

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Abstract

The present invention relates to a 3D model topology mapping method based on deformation driving. The method comprises the following steps: 1) extracting a model file; building a corresponding 3D model according to the model file; 3) searching a mapping state through a heuristic algorithm, wherein the search process is driven by the mapping energy; 4) selecting the optimal mapping state according to the search result; 5) realizing the visualization of the corresponding source model and the target model according to the optimal mapping state; and 6) outputting a result file. Compared to the prior art, the 3D model topology mapping method based on deformation driving is high in efficiency and high in quality of the return result, etc.

Description

A kind of 3d model topology mapping method being driven based on deformation
Technical field
Graphics analysis techniques field of the present invention, is related to a kind of method of 3d model mapping, especially relates to a kind of being based on and become The 3d model topology mapping method that shape drives, is a kind of other model mapping processing Geometrical change and change in topology of component-level Method.
Background technology
Model mapping algorithm is a kind of underlying issue in graphics, and it is widely used in the demand of multiple fields, than In terms of object identification, cluster, model deformation, static modelling etc..For a long time, model mapping algorithm has caused people's Pay attention to, also emerge substantial amounts of correlational study.Before, the classical related algorithm great majority of model mapping problems all focus on tool There are the model that particular geometric requires, " the robust global registration " of such as gelfand et al., zhang et al. " deformation-driven shape correspondence ", " non-rigid of huang et al. registration under isometric deformations》.For these algorithms, when two model gaps increase When big, mapping search will be more difficult to, and the computation complexity of algorithm also can be accordingly higher.
In recent years, the proposition with this field new ideas and a variety of technological innovation, some new algorithms also occur therewith.Than Related kim, " the learning part-based templates from large of v.g et al. as machine learning Collections of 3d shapes " and van et al. " unsupervised co-segmentation of a set of Shapes via descriptor-space spectral clustering ", " the fit and diverse:set of xu et al. evolution for inspiring 3d shape galleries》.But the result that generally speaking, these methods return is relatively For more coarse and dispersion, particularly with there is topological distorted pattern to high-quality mapping cannot be drawn.
Content of the invention
The purpose of the present invention is exactly to overcome the defect of above-mentioned prior art presence to provide a kind of efficiency high, return knot The high 3d model topology mapping method being driven based on deformation of fruit quality.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of 3d model topology mapping method being driven based on deformation, is comprised the following steps:
1) extraction model file；
2) corresponding 3d model is set up according to described model file；
3) mapping status is searched for by heuristic algorithm, described search procedure is driven by Mapping Energy；
4) select optimum mapping status according to Search Results；
5) visualization of corresponding source model and object module is realized according to described optimum mapping status；
6) output result file.
Described model file includes node structure information, attachment structure information and component information, described node structure information Including model fine-grained divided parts information, described attachment structure information includes the physical connection information between each part, institute State component information and include model topology tectonic information.
Described step 2) in, when setting up 3d model, each divided parts structure in analytical model file, according to model literary composition Segmented mode in part obtains fine-grained part.
Described step 3) in search procedure particularly as follows:
301) heuritic approach construction search tree, a kind of search condition of each node on behalf on this search tree, described search Strand state includes Mapping Energy, mapping means and non-mapping means；
302) pass through Mapping Energy type of drive and expand node, when carrying out node expansion, only expand mapping consumption minimum Front k child node, each child node increases the mapping of at least one part on the basis of father node；
303) when the branch line of all search all reaches leaf node, search procedure terminates, and obtains fullpath.
In described heuritic approach, for each node, in search condition space, each searching position is commented Estimate, scan at optimal location.
In described search procedure, the factor of the estimation function consideration that each node scans for includes Mapping Energy, source portion Part to the pantograph ratio of target component and each part with respect to estimation function participation weights.
The estimation formulas of described Mapping Energy are:
E=ed+wc*ec+ws*es
Wherein, e is Mapping Energy, and ec is connectivity changing value, and es is change in topology value, wc and ws is weighted value.
Described destination file includes work summary, journal file, mapped file and mapping graph.
Compared with prior art, the invention has the advantages that
(1) the mapping consumption that the present invention is driven by the use of deformation carries out suggestive search as reference frame, by control Optimal solution quantity processed to carry out child node beta pruning in each search step, and finally gives optimal case it is achieved that more precisely Mapping Energy estimate mode.
(2) when the present invention sets up 3d model according to model file, Model Abstraction is the structure chart comprising curve and surface, fall The interference that the complex properties of low true model are brought to algorithm.
(3), when the present invention utilizes heuristic, for each search node, provide in search condition space to each Individual searching position is estimated, and obtains best position, then scans for until target from this position, so can save bigger Measure meaningless searching route, improve efficiency.
(4) factor that the estimation function of present invention search considers includes Mapping Energy, the scaling of source block to target component With respect to the participation weights of estimation function, give the different weights that different parts are with respect to estimation function than with each part, energy Enough more neatly carry out model mapping.
(5) when the present invention scans for, carried out using prune approach, only retain current Mapping Energy in each expansion Little front k child node, can improve efficiency of algorithm with effective control search space.
Brief description
Fig. 1 is the structural representation of the present invention；
Fig. 2 is for arranging the time of k value during beta pruning；
Fig. 3 is the cost time under each estimation function.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention be not limited to Following embodiments.
As shown in figure 1, the present embodiment provides a kind of 3d model topology mapping method driving based on deformation, specifically describe such as Under.
Provide input: source model m, object module m '.
The first step, extraction model file, the specifying information such as extraction model geometry, topological structure, position attribution.Model File is inputted with xml document, and model file contains partitioning site and the connection of figure, including node structure information, attachment structure Information and component information, node structure information includes model fine-grained divided parts information, and attachment structure information includes each portion Physical connection information between part, component information includes model topology tectonic information.In each structure, there is detailed geometry Information is (as the three-dimensional coordinate put, name of parts, node type etc..
Second step, corresponding 3d model is set up according to model file.According to file content, each of analytical model is drawn Sub-unit structure, obtains fine-grained part according to the segmented mode in model file.Model Abstraction is to comprise curve and table The structure chart in face, the method for structural texture figure " topology-varying 3d shape as the method for alhashim et al. Creation via structural blending ", wherein, curve describes one-dimensional model data, and surface then describes two Dimension module data.Through such abstract model, the interference that the complex properties of true model are brought can be reduced to algorithm.
In this example, modular construction refers to: by certain (usually common-sense) criterion model is carried out drawing in itself Point.For example in seat model, the parts such as backrest, seat cushion, handrail, chair leg can be greatly classified into.Divide by mould in example Type file is given.
3rd step, mapping status is searched for by heuristic algorithm, search procedure drives by Mapping Energy.Search procedure will be led to Cross heuristic algorithm search mapping status, and find out the solution meeting demand condition.In search procedure, searched with heuristic construction Suo Shu, constantly expands node, until finding fullpath.Expand node reference be Mapping Energy, search procedure particularly as follows:
301) heuritic approach construction search tree, a kind of search condition of each node on behalf on this search tree, search for shape State includes Mapping Energy, mapping means and non-mapping means；
302) pass through Mapping Energy type of drive and expand node, when carrying out node expansion, only expand mapping consumption minimum Front k child node, is expanded using prune approach, and each child node increases at least one portion on the basis of father node Part maps；
303) when the branch line of all search all reaches leaf node, search procedure terminates, and obtains fullpath, fullpath Refer to, the paths from source node to target in search tree.The end of fullpath must be the leaf node in search tree.
Wherein, leaf node refers to, does not have the search node of child node, and in leaf node, all parts of source model are all It is mapped as a certain part in object module.
Wherein, beta pruning refers to, in each search step, the quantity of siding stopping node when next layer of search tree is expanded. In this example, set an integer k (k > 0), then only retain the minimum front k son section of current Mapping Energy in each expansion Point.In this way, efficiency of algorithm can be improved with effective control search space.
In the heuristic of application in this example, for each search node, provide in search condition space to every One searching position is estimated, and obtains best position, then scans for until target from this position.So can omit Meaningless searching route in a large number, improves efficiency.
In heuristic search, the appraisal to position is highly important.Employ different appraisals can have different Effect.
Can adopt following evaluation function:
F'(n)=g'(n)+h'(n)
Wherein, f'(n) it is evaluation function；G'(n) it is the minimum Mapping Energy value that starting point arrives node n；H'(n it is) n to mesh The inspiration value of target minimum Mapping Energy.
In search procedure, the factor of the estimation function consideration that each node scans for includes Mapping Energy, source block arrives The pantograph ratio of target component and each part are with respect to the participation weights of estimation function.
Wherein, Mapping Energy refers to the cost it will be appreciated that for mapping.For instinctively, Mapping Energy is less, and this is described It is less with the deformation that object module corresponding component brings to be mapped in source model, also just closer to our needs.In general, calculate Method needs to ensure not changing the properties of artwork: such as symmetry, relative position, connectivity in conversion process.Specifically Say, Mapping Energy is determined by several factors.In " deformation-driven topology-varying 3d shape Correspondence " in, estimate that the function of Mapping Energy is:
E=ed+wc*ec+ws*es
Wherein, ed refers to geometry deformation.Geometry deformation refer to map before and after the change such as the position of corresponding point, shape.In order to weigh Measure this parameter, in function, each part is sampled, and by the Geometrical change between relatively corresponding sampled point Lai really Determine deformation.In this example, px and py represents two corresponding parts respectively.Ec refers to that connectivity changes, and connectivity becomes Change mainly for the relative position change being connected between part.Es refers to change in topology.Change in topology refers in mapping process The situation that number of components changes, such as divides or merges.Wc is the weighted value of ec, and ws is the weighted value of es.
Pantograph ratio esclRefer to source block to the scaling multiple of target component:
escl=min { v (px)/v (py), 2-v (px)/v (py) }
Wherein px and py represents two corresponding parts respectively, and v represents part geometric volume.
In existing main stream approach, all of part is coequally treated.But for typically semanteme, these parts Usually have different importances.Therefore, the present invention gives one weights of each part to determine its ginseng to estimation function With degree.In this example, this weights is determined by the geometric volume of part.
4th step, select optimum mapping status according to Search Results, optimum mapping status refers to spend Mapping Energy minimum Mapping scheme.Specific as follows:
Solution=arg min energy (c) | c ∈ leafnode }
Wherein, energy (c) represents the Mapping Energy of node c, and leafnode represents leaf segment point set.
5th step, by source model, object module, mapping situation visualization display, to reach intuitivism apprehension.In this reality In example, visualization is mainly for model.
6th step, write the result into file and store.Result includes work summary, journal file, mapped file, mapping Figure.Work summary (job) includes the position of remaining destination file, job number, and optimum mapping expends and optimum mapping scheme；Daily record File (log) includes source model file, object module file, optimum mapping consuming；Mapped file (.match) have recorded optimum Mapping scheme, provides a pair of part mapping in every a line of mapped file；Mapping graph (.png) provides mapping with visual means The directviewing description of scheme, color identical part represents them and maps relatively.
According to above-mentioned steps, model data is tested and is analyzed, this test set be have chosen 20 pairs of models and enters Row test, as shown in table 1.All tests are realized all on pc computer, and the major parameter of this pc computer is: 2 centre Reason device intel (r) core (tm) i5-2450m cpu@2.50ghz, internal memory 8gb.
Table 1
Result shows, the inventive method is a kind of efficient and high-quality Model Matching algorithm, and has introduced pantograph ratio There is improvement effect with part weights for some examples, and spend the time to increase, as shown in Figure 2.Sum it up, these test table Bright, suitable estimation function is extremely important to result.
In following experiment, use different estimation functions:
Estimation function 1 selected parameter ed=1.1, es=0.4, ec=0.7；
Estimation function 2 selects ed=1.1, es=0.4, ec=0.7, ed=0.2, escl=0.3；
Ed=1.1 in estimation function 3, es=0.4, ec=0.6, ed=0.3, escl=0.2；
The parameter of estimation function 4 selects same 3, and introduces part weights.
Mapping Energy under above-mentioned estimation function is as shown in table 2.
Table 2
Example number Estimation function 1 Estimation function 2 Estimation function 3 Estimation function 4
1 0.1658 0.1423 0.1322 0.1211
2 0.1375 0.1342 0.1221 0.1321
3 0.1653 0.0912 0.1322 0.1232
4 0.121468 0.1221 0.1203 0.1123
5 0.0915 0.0812 0.0678 0.0622
6 0.10322 0.0823 0.0921 0.0812
7 0.0478 0.0432 0.0232 0.0221
8 0.10853 0.1008 0.1123 0.1036
9 0.14445 0.1232 0.1321 0.1123
10 0.0825 0.1121 0.1012 0.0982
11 0.08217 0.0762 0.0621 0.0528
12 0.0405 0.0213 0.0423 0.0321
13 0.09404 0.0621 0.0452 0.0423
14 0.140699 0.0921 0.1219 0.1125
15 0.16525 0.1219 0.1148 0.1123
16 0.121468 0.1123 0.1021 0.1024
17 0.18288 0.1387 0.1432 0.1323
18 0.1234 0.1212 0.1172 0.1134
19 0.13306 0.1421 0.1321 0.1343
20 0.154673 0.0982 0.1321 0.1068
In beta pruning arrange different value of K, in test due to choose be optimal solution, so substantially not having on solution quality Too big change, but it can be seen that k value arranges the impact to the time, as shown in Figure 3.

Claims (8)

1. a kind of 3d model topology mapping method being driven based on deformation is it is characterised in that comprise the following steps:
1) extraction model file；
2) corresponding 3d model is set up according to described model file；
3) mapping status is searched for by heuristic algorithm, described search procedure is driven by Mapping Energy；
4) select optimum mapping status according to Search Results；
5) visualization of corresponding source model and object module is realized according to described optimum mapping status；
6) output result file.
2. the 3d model topology mapping method being driven based on deformation according to claim 1 is it is characterised in that described model File includes node structure information, attachment structure information and component information, and it is fine-grained that described node structure information includes model Divided parts information, described attachment structure information includes the physical connection information between each part, and described component information includes mould Type topological structure information.
3. the 3d model topology mapping method being driven based on deformation according to claim 1 is it is characterised in that described step 2) in, when setting up 3d model, each divided parts structure in analytical model file, obtains according to the segmented mode in model file Obtain fine-grained part.
4. the 3d model topology mapping method being driven based on deformation according to claim 1 is it is characterised in that described step 3) search procedure in particularly as follows:
301) heuritic approach construction search tree, a kind of search condition of each node on behalf on this search tree, described search shape State includes Mapping Energy, mapping means and non-mapping means；
302) node is expanded by Mapping Energy type of drive, when carrying out node expansion, only expand mapping and consume minimum front k Child node, each child node increases the mapping of at least one part on the basis of father node；
303) when the branch line of all search all reaches leaf node, search procedure terminates, and obtains fullpath.
5. the 3d model topology mapping method being driven based on deformation according to claim 4 is it is characterised in that described inspiration In formula algorithm, for each node, in search condition space, each searching position is estimated, enters at optimal location Line search.
6. the 3d model topology mapping method being driven based on deformation according to claim 4 is it is characterised in that described search During, the factor of the estimation function consideration that each node scans for includes Mapping Energy, the contracting of source block to target component Put than with each part with respect to estimation function participation weights.
7. the 3d model topology mapping method being driven based on deformation according to claim 6 is it is characterised in that described mapping The estimation formulas of energy are:
E=ed+wc*ec+ws*es
Wherein, e is Mapping Energy, and ec is connectivity changing value, and es is change in topology value, wc and ws is weighted value.
8. the 3d model topology mapping method being driven based on deformation according to claim 1 is it is characterised in that described result File includes work summary, journal file, mapped file and mapping graph.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103871074A (en) * 2012-12-18 2014-06-18 帕洛阿尔托研究中心公司 Simultaneous mapping and registering thermal images
US20160210500A1 (en) * 2015-01-15 2016-07-21 Samsung Electronics Co., Ltd. Method and apparatus for adjusting face pose

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103871074A (en) * 2012-12-18 2014-06-18 帕洛阿尔托研究中心公司 Simultaneous mapping and registering thermal images
US20160210500A1 (en) * 2015-01-15 2016-07-21 Samsung Electronics Co., Ltd. Method and apparatus for adjusting face pose

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
IBRAHEEM ALHASHIM等: "Deformation-Driven Topology-Varying 3D Shape Correspondence", 《ACM TRANSACTIONS ON GRAPHICS》 *

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