New descriptors' combination for 3D mesh correspondence and retrieval.

3D models that are widely used nowadays are mostly represented by meshes or point clouds. These models are appearing in many fields like computer vision, informatics, engineering, as well as medicine. In this paper, we aim to find a superior one-to-one correspondence between 3D models in order to obtain optimal matching and retrieval. To do so, we detect feature points using the well known 3D Harris detector, followed by proposing a combination of local shape descriptors to form a compact feature vector for the keypoints extracted that consist of: Gaussian curvature, curvature index, and shape index. Lastly we model the matching problem as combinatorial problem solved using brute-force approach, and Hungarian one, comparing the efficiency between them. Our proposed combination of descriptors show good performance and compromise numerical values specifically using the Hungarian algorithm where its results demonstrate our proposed approach. Moreover, cosine similarity is used behind the retrieval system between these features of each pairs in the database, and our system gives accurate retrieval for several models, and acceptable percentages for others.