  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.
