  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.
