  In the past decades, feature-learning-based 3D shape retrieval approaches
have been received widespread attention in the computer graphic community.
These approaches usually explored the hand-crafted distance metric or
conventional distance metric learning methods to compute the similarity of the
single feature. The single feature always contains onefold geometric
information, which cannot characterize the 3D shapes well. Therefore, the
multiple features should be used for the retrieval task to overcome the
limitation of single feature and further improve the performance. However, most
conventional distance metric learning methods fail to integrate the
complementary information from multiple features to construct the distance
metric. To address these issue, a novel multi-feature distance metric learning
method for non-rigid 3D shape retrieval is presented in this study, which can
make full use of the complimentary geometric information from multiple shape
features by utilizing the KL-divergences. Minimizing KL-divergence between
different metric of features and a common metric is a consistency constraints,
which can lead the consistency shared latent feature space of the multiple
features. We apply the proposed method to 3D model retrieval, and test our
method on well known benchmark database. The results show that our method
substantially outperforms the state-of-the-art non-rigid 3D shape retrieval
methods.
