Determining Efficient Scan-Patterns for 3-D Object Recognition Using Spin Images

This paper presents a method to determine efficient scanpatterns for spin images using robust multivariate regression. A large dataset is generated using scan-patterns with random radial scanlines through an oriented point and determining the corresponding classification performance. Eight features are chosen, which are used as predictor variables for a multivariate least trimmed squares regression algorithm, achieving an adjusted coefficient of determination of R=0.80. The correlation coefficients are then used in an exemplary cost-benefit function of an exemplary application of the proposed method.