A Novel 3D Shape Retrieval Method Using Point Spatial Distributions along Principal Axis

Abstract

Rapidly increasing 3D shape application has led to the development of content-based 3D shape retrieval research. In this paper, we proposed a new retrieval method. The method is constructed on a spatial distribution computation of sampling points on the surface of 3D shape. The contribution is that we use an inner cylinder to contain the points distributed nearer on the largest principal axis, and its radius is the average distance of points to the largest principal axis. And then we compute the point spatial distribution by partitions of the minimum bounding box and the inner cylinder. We have examined our method on a 3D shape database of general objects from Princeton Shape Benchmark and confirmed its efficiency. We also compared this method with other similar methods on the same shapes database from Princeton Shape Benchmark, and it achieved better retrieving precision. This method can be used to extract the feature of 3D shapes, classify 3D shapes and retrieve similar shapes in shapes database.