In this paper, we address the 3D point cloud based retrieval problem for both non-rigid and rigid 3D data. As powerful computation resources and scanning devices have led to an exponential growth of 3D point cloud data, retrieving the relevant 3D objects from databases is a challenging task. The local descriptors provide only the abstract representations that do not enable the exploration of shape variability to solve the 3D object retrieval problem. Thus, it is not just the local descriptors but also the encoding of local signatures into global descriptors which is of crucial importance for enhancing the performance. To create a compact shape signature that constitutes the 3D object as a whole, various encoding techniques have been proposed in the literature. The most popular among them are bag-of-features , Fisher vector  and vector of locally aggregated descriptors . However evaluating the different encoding techniques and analyzing the critical aspects to boost the performance of 3D point cloud retrieval is still an unsolved problem. We propose to provide an exhaustive evaluation of the different encoding techniques when combined with local feature descriptors for solving non-rigid and rigid point cloud retrieval task. We fix improved wave kernel signature and metric tensor & Christoffel symbols local descriptors specifically built for non-rigid and rigid data as given in  and  respectively. We also present a consistent comparative analysis of our method with the existing benchmarks, the results of which illustrate the robustness of the proposed approach on point cloud data.