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.

翻译：评估三维绘图性能并非易事,因为现有指标要求只有用特殊工具才能收集的地面真象数据。在本文件中,我们为这一评估建议了一种高度、密集的地图后部(DMP)数据。它可以在没有任何地面真象数据的情况下发挥作用。相反,它从密集的云层观测中计算了一个可比值,反映了地图后部概率。在我们的实验中,拟议的DMP以地面实象衡量标准为基准。结果显示DMP可以提供类似的评价能力。拟议的指标使得评估不同方法更加灵活,并开辟了许多新的可能性,例如自监督的方法和更多的可用数据集。