Sparse patch coding for 3D model retrieval

3D shape retrieval is a fundamental task in many domains such as multimedia, graphics, CAD, and amusement. In this paper, we propose a 3D object retrieval approach by effectively utilizing low-level patches with initial semantics of 3D shapes, which are similar as superpixels in images. These patches are first obtained by means of stably over-segmenting 3D shape, and we adopt five representative geometric features such as shape diameter function, average geodesic distance, and heat kernel signature, to characterize these low-level patches. A large number of patches collected from shapes in a dataset are encoded into visual words by virtue of sparse coding, and input query compares with 3D models in the dataset by probability distribution of visual words. Experiments show that the proposed method achieves comparable retrieval performance to state-of-the-art methods.