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We introduce an online 2D-to-3D semantic instance mapping algorithm aimed atgenerating comprehensive, accurate, and efficient semantic 3D maps suitable forautonomous agents in unstructured environments. The proposed approach is basedon a Voxel-TSDF representation used in recent algorithms. It introduces novelways of integrating semantic prediction confidence during mapping, producingsemantic and instance-consistent 3D regions. Further improvements are achievedby graph optimization-based semantic labeling and instance refinement. Theproposed method achieves accuracy superior to the state of the art on publiclarge-scale datasets, improving on a number of widely used metrics. We alsohighlight a downfall in the evaluation of recent studies: using the groundtruth trajectory as input instead of a SLAM-estimated one substantially affectsthe accuracy, creating a large gap between the reported results and the actualperformance on real-world data.

Volumetric Semantically Consistent 3D Panoptic Mapping