Iterative closest point (ICP) algorithm is widely used in multi-view fine registration of 3D point clouds, while its accuracy and convergence to global optimization depend on initial registration position. It fails when a great difference exists to initial position of the waited registered point clouds. Coarse registration aims to provide a good initial registration position for ICP. A new coarse registration algorithm—iterative least space distribution entropy is proposed based on the space distribution of point clouds, and the concept of entropy is used for describing this distribution law according to information theory. Experiments show that the proposed algorithm can offer a good initial registration position for ICP and it owns a high efficiency and can realize registration without using ICP under precision permission.