In the past decade, the number of available 3D models has grown rapidly. This growth is mainly driven by the development of scanning devices and increasing demand for 3D meshes in many application fields. As a consequence, it becomes crucial to have consistent content-based retrieval systems of large 3D mesh repositories. However, many existing methods still struggle to achieve a good compromise between efficiency and accuracy, especially on large and heterogeneous databases. In this work, we propose a supervised framework for building compact but discriminative 3D shape descriptors. For each mesh, we start by computing three features - the dihedral angles between adjacent faces, the Shape Diameter Function (SDF), and the Shape Index - then we convert them into normalized histograms and concatenate them into a single feature vector, which is then used as input to an Explainable Boosting Classifier (EBC). After training, the classifier produces, for each mesh, a short probability vector that we use as its numerical descriptor. Experiments on the standard Princeton Benchmark database validate our approach, achieving a mean Average Precision (mAP) of 97.23%, outperforming the selected baseline methods under the adopted experimental protocol.
Zakani et al. (Thu,) studied this question.