A Fast 3D Retrieval Algorithm via Class-Statistic and Pair-Constraint Model

With the development of 3D technologies and devices, 3D model retrieval becomes a hot research topic where multi-view matching algorithms have demonstrated satisfying performance. However, exciting works overlook the common factors among objects in a single class, and they are time consuming in retrieval processing. In this paper, a class-statistics and pair-constraint model (CSPC) method is originally proposed for 3D model retrieval, which is composed of supervised class-based statistics model and pair-constraint object retrieval model. In our CSPC model, we firstly convert view-based distance measure into object-based distance measure without falling in performance, which will advance 3D model retrieval speed. Secondly, the generality of the distribution of each feature dimension in each class is computed to judge category information, and then we further adopt this distribution information to build class models. Finally, an object-based pairwise constraint is introduced on the base of the class-statistic measure, which can remove a lot of false alarm samples in retrieval. Experimental results on ETH, NTU-60, MVRED and PSB 3D datasets show that our method is fast, and its performance is also comparable with the-state-of-the-art algorithms.