  It is not easy when evaluating 3D mapping performance because existing
metrics require ground truth data that can only be collected with special
instruments. In this paper, we propose a metric, dense map posterior (DMP), for
this evaluation. It can work without any ground truth data. Instead, it
calculates a comparable value, reflecting a map posterior probability, from
dense point cloud observations. In our experiments, the proposed DMP is
benchmarked against ground truth-based metrics. Results show that DMP can
provide a similar evaluation capability. The proposed metric makes evaluating
different methods more flexible and opens many new possibilities, such as
self-supervised methods and more available datasets.
