  Learning universal representations across different applications domain is an
open research problem. In fact, finding universal architecture within the same
application but across different types of datasets is still unsolved problem
too, especially in applications involving processing 3D point clouds. In this
work we experimentally test several state-of-the-art learning-based methods for
3D point cloud registration against the proposed non-learning baseline
registration method. The proposed method either outperforms or achieves
comparable results w.r.t. learning based methods. In addition, we propose a
dataset on which learning based methods have a hard time to generalize. Our
proposed method and dataset, along with the provided experiments, can be used
in further research in studying effective solutions for universal
representations. Our source code is available at:
github.com/DavidBoja/greedy-grid-search.
