Standard 3D reconstruction pipelines assume stationary world, therefore
suffer from `ghost artifacts' whenever dynamic objects are present in the
scene. Recent approaches has started tackling this issue, however, they
typically either only discard dynamic information, represent it using bounding
boxes or per-frame depth or rely on approaches that are inherently slow and not
suitable to online settings.
We propose an end-to-end system for live reconstruction of large-scale
outdoor dynamic environments. We leverage recent advances in computationally
efficient data-driven approaches for 6-DoF object pose estimation to segment
the scene into objects and stationary `background'. This allows us to represent
the scene using a time-dependent (dynamic) map, in which each object is
explicitly represented as a separate instance and reconstructed in its own
volume. For each time step, our dynamic map maintains a relative pose of each
volume with respect to the stationary background. Our system operates in
incremental manner which is essential for on-line reconstruction, handles
large-scale environments with objects at large distances and runs in (near)
real-time. We demonstrate the efficacy of our approach on the KITTI dataset,
and provide qualitative and quantitative results showing high-quality dense 3D
reconstructions of a number of dynamic scenes.

该研究提出了一种实时重建大规模戶外动态环境的端到端系统，可以在维持追踪动态物体姿态信息和保证实时运行的情况下，提供具有高质量的密集 3D 重建效果。