Non-rigid registration is a crucial step for many applications such as motion tracking, model retrieval, and object recognition. The accuracy of these applications is highly dependent on the initial position used in registration step. In this paper we propose a novel Convex Hull Aided Coarse Registration refined by two algorithms applied on projected points. Firstly, the proposed approach uses a statistical method to find the best plane that represents each point cloud. Secondly, all the points of each cloud are projected onto the corresponding planes. Then, two convex hulls are extracted from the two projected point sets and then matched optimally. Next, the non-rigid transformation from the reference to the model is robustly estimated through minimizing the distance between the matched point's pairs of the two convex hulls. Finally, this transformation estimation is refined by two methods. The first one is the refinement of coarse registration by Iterative Closest Point (ICP). The second one consists of the refinement of coarse registration by the Normal Distribution Transform (NDT). An experimental study, carried out on several clouds, shows that the refinement of coarse registration with ICP gives, in the most cases, a better result than refinement with NDT.