ボリュメトリックな意味的一貫性を持つ3Dパノプティックマッピング
Volumetric Semantically Consistent 3D Panoptic Mapping
Voxel-TSDF表現を用い、意味予測の信頼度を統合して意味的・インスタンス的に一貫した3D領域を生成するオンライン2D-to-3Dマッピング手法を提案し、大規模データセットで高精度を達成した。
著者: Yang Miao, Iro Armeni, Marc Pollefeys, Daniel Barath
分類: cs.RO, cs.CV
原文アブストラクト
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.
