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It is not easy when evaluating 3D mapping performance because existingmetrics require ground truth data that can only be collected with specialinstruments. In this paper, we propose a metric, dense map posterior (DMP), forthis evaluation. It can work without any ground truth data. Instead, itcalculates a comparable value, reflecting a map posterior probability, fromdense point cloud observations. In our experiments, the proposed DMP isbenchmarked against ground truth-based metrics. Results show that DMP canprovide a similar evaluation capability. The proposed metric makes evaluatingdifferent methods more flexible and opens many new possibilities, such asself-supervised methods and more available datasets.

A metric for evaluating 3D reconstruction and mapping performance with no ground truthing