3D Shape Descriptor Based on 3D Fourier Transform

In this paper, we propose a new method for describing 3D-shape in order to perform similarity search for polygonal mesh models. The approach is based on characterization of spatial properties of 3D-objects by suitable feature vectors, i.e., the goal is to define 3D-shape descriptors in such a way that similar objects are represented by "close" points in the feature vector space. We present a descriptor which is invariant with respect to translation, rotation, scaling, and reflection and robust with respect to level-ofdetail. A coarse voxelization of a 3D-model is used as the input for the 3D Discrete Fourier Transform (3D DFT), while the absolute values of obtained (complex) coefficients are considered as components of the feature vector. Multiple levels of abstraction of the feature are embedded by the applied transform. The performance of the proposed method is compared to some previous approaches by means of precision/recall tests. Generally, results show that the new approach introduces improvements in the 3D-model retrieval process