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The Research Of Model Repairing Based On Geometric Texture Synthesis

Posted on:2019-07-07Degree:MasterType:Thesis
Country:ChinaCandidate:X Y XuFull Text:PDF
GTID:2428330566993483Subject:Mechanical engineering
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With the popularization of 3D scanning and measuring equipment,digital geometric processing technology has been widely in various fields.The incomplete defects in the model itself and the technical constraints,inevitably make some features of model surface missing.And geometric texture is regarded as one kind of the missing features,its synthesis technology particularly plays an important role in hole-repairing research of model.The texture synthesis technology is applied to hole-repairing research of model,which could not only reduce the operating cost of traditional technology,but also effectively establish the foundation for subsequent geometry processing.In this paper,several problems in mesh model hole-repairing technology are studied.The main research contents and achievements are as follows:(1)The Laplace coordinate is flexibly applied to the feature editing operation of spatial grids.Combined with multi-resolution thought theory,the difference of Laplace coordinate between two,whether the mesh vertices are in the state of texture feature or not,could be characterized the local details of geometric texture features.The normal vector and mean curvature component information of sampling points' Laplace coordinate inside the sample blocks,can be constructed as the similarity evaluation criteria.After the best matching texture feature is migrated,the Laplace coordinate of original hole area grid are resetting.Based on the differential coordinates grid deformation framework,the Cartesian coordinates of mesh vertices in the hole region of mesh are reconstructed by solving a linear system with constraints.The repair of texture features in the hole area is realized.(2)A self-adaptive sampling method which doesn't depend on the neighborhood information of mesh vertices is proposed.Because the neighbor topology information of grid vertices can't be absolutely same,it is difficult to directly sample based on grid vertices.The adaptive sampling method which isn't dependent on the domain information of grid vertices is that geometric sampling of the plane is done after the grid parameterization.During sample block sampling within the parameter domain,first of all,the current vertex is served as seed point which is the center of block.Simulating a square shape of spiral array,the next point moves the unit distance along the horizontal or vertical direction relative to the previous one.The geometric information representing texture feature would be obtained by interpolation.Then the sample block sampling of current vertex is completed.(3)A new algorithm of texture synthesis based on sample block is proposed to realize the restoration of model feature details.As the idea of texture synthesis is introduced,that image texture synthesis theory is expanded to the three-dimensional space.For the texture synthesis based on sample block,first of all,the characteristic of texture details is analyzed to determine the area size and information of sample block unit.Then the seed sample block on the hole boundary is selected.After that,the sample block,whose update order is adjacent to the seed sample block,is matched to the best similar sample block.And the feature migration would be completed.Last but not least,the interior of hole boundary are continually updated and shrink.Hole repairing is accomplished in a spiral path manner.A hole-repairing method based on texture synthesis is put forward,mainly involving texture information expression,sample block sampling,texture information matching,feature information migration and texture feature reconstruction.Considering the relevant theories of geometric texture synthesis technology,the hole-repairing of 3D texture model is completed which verified the effectiveness and robustness of algorithm in manner of model instantiation.
Keywords/Search Tags:Model Hole-repairing, Texture Information, Sample Block Sampling, Information Matching, Feature Migration
PDF Full Text Request
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