動的環境におけるリアルタイム意味マッピングのためのデータセットとネットワーク MotionSC
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
動的物体を含む屋外シーンの意味的シーン補完(SSC)用データセットを構築し、時間情報を活用したリアルタイム高密度意味マッピングネットワークMotionSCを提案した。
著者: Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari
分類: cs.CV, cs.RO
原文アブストラクト
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions or traces. We create SSC baselines from state-of-the-art open source networks and construct a benchmark real-time dense local semantic mapping algorithm, MotionSC, by leveraging recent 3D deep learning architectures to enhance SSC with temporal information. Our network shows that the proposed data set can quantify and supervise accurate scene completion in the presence of dynamic objects, which can lead to the development of improved dynamic mapping algorithms. All software is available at https://github.com/UMich-CURLY/3DMapping.
