Scale Matching of 3D Point Clouds by Finding Keyscales with Spin Images
Toru Tamaki, Shunsuke Tanigawa, Yuji Ueno, Bisser Raytchev, Kazufumi Kaneda · 2010
In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scales. To match scales, we propose a keyscale that characterizes the scale of a given 3D point cloud. By performing PCA of spin images over different scales, a keyscale is defined as the scale that gives the minimum of cumulative contribution rate of PCA at a specific dimension of eigenspace. Simulations with the Stanford bunny and experimental results with 3D reconstructions of a real scene demonstrate that keyscales of any 3D point clouds can be uniquely found and effectively used for scale matching.