We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at
generating comprehensive, accurate, and efficient semantic 3D maps suitable for
autonomous agents in unstructured environments. The proposed approach is based
on a Voxel-TSDF representation used in recent algorithms. It introduces novel
ways of integrating semantic prediction confidence during mapping, producing
semantic and instance-consistent 3D regions. Further improvements are achieved
by graph optimization-based semantic labeling and instance refinement. The
proposed method achieves accuracy superior to the state of the art on public
large-scale datasets, improving on a number of widely used metrics. We also
highlight a downfall in the evaluation of recent studies: using the ground
truth trajectory as input instead of a SLAM-estimated one substantially affects
the accuracy, creating a large gap between the reported results and the actual
performance on real-world data.

我们介绍了一种在线的二维到三维语义实例映射算法，旨在生成适用于非结构化环境中的自主代理的全面、准确且高效的语义三维地图。该方法基于最近算法中使用的 Voxel-TSDF 表示，引入了在映射过程中集成语义预测置信度的新方法，产生了语义和实例一致的三维区域。通过基于图优化的语义标记和实例细化，进一步提高了准确性。该方法在公共大规模数据集上的准确性优于现有技术，改进了一些广泛使用的度量标准。我们还指出了最近研究评估中的一个问题：使用真实轨迹作为输入而不是 SLAM 估计的轨迹，这对准确性产生了重大影响，导致了报告结果与实际在实际数据上的性能之间存在很大差距。