LAUSR

Isometry has been widely used in corresponding problem with pose variation. However, most existing methods are causing wrong correspondences due to the ambiguity of geodesic distance. This article introduces the semantic isometry to three-dimensional shape correspondence and proposes a new framework called detection–recognition–correspondence. The idea of semantic isometry is to embed the semantic information and statistical learning through sparse correspondence for better performance. Feature point detection is first utilized to extract the salient feature point of the three-dimensional shapes. Then, instead of finding correspondence pairs directly by minimizing the isometric error of the detected feature points, the semantic labels of these feature points are recognized using the support vector machine. The semantic label is used to perform a priority-driven isometric correspondence.The highly reliable corresponding pairs are then obtained to serve as the further constraint in the following corresponding process. During the experiments, the robustness of the proposed algorithm is verified by different kinds of three-dimensional dynamic models, including some very challenging data with pose variation and missing parts. Moreover, the proposed framework can greatly improve the corresponding accuracy over the existing state-of-the-art algorithms.