The Research Of 3D Shape Correspondence Analysis Based On Spectral Symmetry

With the research object of the 3D geometric model moving towards high-level shape analysis and understanding,aiming at discovering the underling semantic information of a 3D shape.Symmetry analysis is one of the most important problems of geometry processing.Existing approaches to symmetry detection have so far been concerning extrinsic symmetry.However,the intrinsic symmetry is more general in 3D shapes.Its detection is harder since it need to consider both symmetry and segmentation.Meanwhile,it is more complex.Currently,the visualization and availability of 3D shapes have been continuously improved.Shape matching is one of the fundamental problems in computer Graphics.3D models symmetrical matching,which is to establish of local shapes between correspondences and stay structure.It includes point to point matching,sparse spectral matching,elastic net matching,or functional map between two surfaces.It has become one of the extensive research topics in 3D model registration.In this paper,in order to two fundamental problems,i.e.robustness and efficiency,3D shape correspondence analysis based on spectral symmetry.In particular,the deformation of the shapes avoid right and left flip in high-efficiency and high-precision.Firstly,intrinsic symmetric point pairs of the model are constructed by heat kernel signature(HKS)and geometric constraints,at the same time,remove some redundant points and add a few “helpful points” by the fusion method.Secondly,based on the spectral embedding space analysis,the intrinsic symmetric plane of the model is extracted and the symmetrical properties of the model are effectively identified according to the model surface normal vector,getting intrinsic symmetry point pair.Therefore the consistent spectral symmetry structure of the model is presented.Finally,combining the Coherent Point Drift method,the shape registration of non-rigid model based on spectral symmetry is implemented.The experimental results show that the matching method is efficient and robust to the non-rigid deformable shape matching.Moreover,the structural features in same category models also are effectively identified.It provides a solid theoretical basis for the application of model compression,retrieval,and reconstruction.