  A novel solution for the content-based 3D shape retrieval problem using an
unsupervised clustering approach, which does not need any label information of
3D shapes, is presented in this work. The proposed shape retrieval system
consists of two modules in cascade: the irrelevance filtering (IF) module and
the similarity ranking (SR) module. The IF module attempts to cluster gallery
shapes that are similar to each other by examining global and local features
simultaneously. However, shapes that are close in the local feature space can
be distant in the global feature space, and vice versa. To resolve this issue,
we propose a joint cost function that strikes a balance between two distances.
Irrelevant samples that are close in the local feature space but distant in the
global feature space can be removed in this stage. The remaining gallery
samples are ranked in the SR module using the local feature. The superior
performance of the proposed IF/SR method is demonstrated by extensive
experiments conducted on the popular SHREC12 dataset.
