  We present SplitFusion, a novel dense RGB-D SLAM framework that
simultaneously performs tracking and dense reconstruction for both rigid and
non-rigid components of the scene. SplitFusion first adopts deep learning based
semantic instant segmentation technique to split the scene into rigid or
non-rigid surfaces. The split surfaces are independently tracked via rigid or
non-rigid ICP and reconstructed through incremental depth map fusion.
Experimental results show that the proposed approach can provide not only
accurate environment maps but also well-reconstructed non-rigid targets, e.g.
the moving humans.
