Invariant extraction and segmentation of 3D objects using linear lie algebra model

This paper first presents robust algorithms to extract invariants of the linear Lie algebra model from 3D objects. In particular, an extended 3D Hough transform is presented to extract accurate estimates of the normal vectors. The Least square fitting is used to find normal vectors and representation matrices. Then an algorithm of segmentation for 3D objects is shown using the invariants of the linear Lie algebra. Both distributions of invariants, in the invariant space and on the object surface, are used to produce clusters and edge detection.