Investigating the Effects of Multiple Factors Towards More Accurate 3-D Object Retrieval
This paper proposes a novel framework for 3-D object retrieval, taking into account most of the factors that may affect the retrieval performance. Initially, a novel 3-D model alignment method is introduced, which achieves accurate rotation estimation through the combination of two intuitive criteria, plane reflection symmetry and rectilinearity. After the pose normalization stage, a low-level descriptor extraction procedure follows, using three different types of descriptors, which have been proven to be effective. Then, a novel combination procedure of the above descriptors takes place, which achieves higher retrieval performance than each descriptor does separately. The paper provides also an in-depth study of the factors that can further improve the 3-D object retrieval accuracy. These include selection of the appropriate dissimilarity metric, feature selection/dimensionality reduction on the initial low-level descriptors, as well as manifold learning for re-ranking of the search results. Experiments performed on two 3-D model benchmark datasets confirm our assumption that future research in 3-D object retrieval should focus more on the efficient combination of low-level descriptors as well as on the selection of the best features and matching metrics, than on the investigation of the optimal 3-D object descriptor.
3-d object retrieval rotation estimation descriptor extraction feature selection manifold learning