Retrieving of 3D Models using Linear Prediction Coefficients in Cylindrical Projections by Generalizing the Projection to three Perpendicular Cylinders

In the recent years, due to the growth and development of the Internet, 3D model retrieval has become a wide research field. Several methods have been presented to retrieve 3D models. Each method employs a special feature. In this paper, a geometric based method is proposed to retrieve the objects. In 3D models, the adjacent points on the surface of object are highly correlated. So, the linear prediction analysis has a proper performance to estimate these models. In this paper, the linear prediction coefficients are extracted from cylindrical projections of 3D objects as an appropriate descriptor. At first, the object is projected onto the lateral surface of a bounding cylinder. Then, the linear prediction coefficients are extracted from cylindrical projection. To alleviate the occlusion problem, cylindrical projection is generalized to three cylinders along all principal axes. The principal component analysis is employed to normalize the descriptor against rotation. The performance of the proposed descriptor is evaluated employing the McGill database of 3D models. Experimental results demonstrate that the proposed method discriminates the objects with different structure and remains robust against noise as well.