Evaluation of a New Point Clouds Registration Method Based on Group Averaging Features

Registration of point clouds is required in the processing of large biological data sets. The trade off between computation time and accuracy of the registration is the main challenge in this task. We present a novel method for registering point clouds in two and three dimensional space based on Group Averaging on the Euclidean transformation group. It is applied on a set of neighboring points whose size directly controls computing time and accuracy. The method is evaluated regarding dependencies of the computing time and the registration accuracy versus the point density assuming their random distribution. Results are verified in two biological applications on 2D and 3D images.