Self-Supervised Non-Isometric 3D Shape Collection Correspondence Calculation Method
Aiming at the problem of low accuracy and poor generalization ability in existing non-isometric 3D shape collection correspondence calculation methods,a self-supervised non-isometric 3D shape collection correspondence calculation method using deep intrinsic-extrinsic feature alignment algorithm is proposed.Firstly,discriminative feature descriptors are obtained by directly learning the original 3D shape features through DiffusionNet.Then,the deep intrinsic-extrinsic feature alignment algorithm is employed to compute correspondences between non-isometric shapes.Consistency between internal and external information is realized by utilizing local manifold harmonic bases as intrinsic information of the shapes and integrating external information such as Cartesian coordinates.Consequently,correspondence results are generated automatically in an unsupervised manner.Finally,a weighted undirected graph of non-isometric shape collections is constructed.Based on the principle of inherent correlation among similar geometric shapes,a self-supervised multi-shape matching algorithm is designed to continuously enhance the cycle-consistency of the shortest path in the shape graph,and thus optimal correspondences for non-isometric 3D shape collections are obtained.Experimental results demonstrate that the proposed method achieves small geodesic errors in correspondences with accurate results,and effectively deals with the symmetric ambiguity problem with good generalization ability.
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