A new shape descriptor using sliced image histogram for 3D model retrieval

3-dimensional (3D) model retrieval is one of emerging research fields to find the matching shape of a given query from 3D database. This paper proposes a new shape descriptor for 3D model retrieval. A shape descriptor of 3D model requires a model normalization in order to be invariant to translation, rotation, and scale for its model. Our method is based on principal component analysis (PCA) for normalizing all the models. The shape descriptor is using a histogram of 2D images sliced along the x-, y-, and z-coordinates for measuring the similarity in 3D models. Sliced shapes for a 3D model involve a hundred planes to orthogonalize with the x-y-, and z-coordinates, respectively. Therefore, sliced shape is the 2D plane images created by intersecting at between 3D model and the planes. Our approach is to compute the slices of 3D model for the x-y-, and z-coordinates, respectively and to set by the principal axis based on principal component analysis (PCA) in order to match the 3D model between the given query and the database. Experimental results show that the proposed approach outperforms the previous approaches. We demonstrate the proposed 3D retrieval system with an intermediate example at each step.