  Deep learning has shown promising results for multiple 3D point cloud
registration datasets. However, in the underwater domain, most registration of
multibeam echo-sounder (MBES) point cloud data are still performed using
classical methods in the iterative closest point (ICP) family. In this work, we
curate and release DotsonEast Dataset, a semi-synthetic MBES registration
dataset constructed from an autonomous underwater vehicle in West Antarctica.
Using this dataset, we systematically benchmark the performance of 2 classical
and 4 learning-based methods. The experimental results show that the
learning-based methods work well for coarse alignment, and are better at
recovering rough transforms consistently at high overlap (20-50%). In
comparison, GICP (a variant of ICP) performs well for fine alignment and is
better across all metrics at extremely low overlap (10%). To the best of our
knowledge, this is the first work to benchmark both learning-based and
classical registration methods on an AUV-based MBES dataset. To facilitate
future research, both the code and data are made available online.
