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This work addresses a gap in semantic scene completion (SSC) data by creatinga novel outdoor data set with accurate and complete dynamic scenes. Our dataset is formed from randomly sampled views of the world at each time step, whichsupervises generalizability to complete scenes without occlusions or traces. Wecreate SSC baselines from state-of-the-art open source networks and construct abenchmark real-time dense local semantic mapping algorithm, MotionSC, byleveraging recent 3D deep learning architectures to enhance SSC with temporalinformation. Our network shows that the proposed data set can quantify andsupervise accurate scene completion in the presence of dynamic objects, whichcan lead to the development of improved dynamic mapping algorithms. Allsoftware is available at https://github.com/UMich-CURLY/3DMapping.

MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments