Motion estimation is a very important problem in dynamic scene analysis. Although it is easier to estimate motion parameters from 3D data than from 2D images, it is not trivial, since the 3D data we have are almost always corrupted by noise. A comparative study on motion estimation from 3D line segments is presented. Two representations of line segments and two representations of rotation are described. With different representations of line segments and rotation a number of methods for motion estimation are presented, including the extended Kalman filter a general minimization process and the singular value decomposition. These methods are compared using both synthetic and real data obtained by a trinocular stereo. It is observed that the extended Kalman filter with the rotation axis representation of rotation is preferable. Note that all methods discussed can be directly applied to 3D point data.