  Reconstruction of a continuous surface of two-dimensional manifold from its
raw, discrete point cloud observation is a long-standing problem. The problem
is technically ill-posed, and becomes more difficult considering that various
sensing imperfections would appear in the point clouds obtained by practical
depth scanning. In literature, a rich set of methods has been proposed, and
reviews of existing methods are also provided. However, existing reviews are
short of thorough investigations on a common benchmark. The present paper aims
to review and benchmark existing methods in the new era of deep learning
surface reconstruction. To this end, we contribute a large-scale benchmarking
dataset consisting of both synthetic and real-scanned data; the benchmark
includes object- and scene-level surfaces and takes into account various
sensing imperfections that are commonly encountered in practical depth
scanning. We conduct thorough empirical studies by comparing existing methods
on the constructed benchmark, and pay special attention on robustness of
existing methods against various scanning imperfections; we also study how
different methods generalize in terms of reconstructing complex surface shapes.
Our studies help identify the best conditions under which different methods
work, and suggest some empirical findings. For example, while deep learning
methods are increasingly popular, our systematic studies suggest that,
surprisingly, a few classical methods perform even better in terms of both
robustness and generalization; our studies also suggest that the practical
challenges of misalignment of point sets from multi-view scanning, missing of
surface points, and point outliers remain unsolved by all the existing surface
reconstruction methods. We expect that the benchmark and our studies would be
valuable both for practitioners and as a guidance for new innovations in future
research.
