  Recently, neural network for scene flow estimation show impressive results on
automotive data such as the KITTI benchmark. However, despite of using
sophisticated rigidity assumptions and parametrizations, such networks are
typically limited to only two frame pairs which does not allow them to exploit
temporal information. In our paper we address this shortcoming by proposing a
novel multi-frame approach that considers an additional preceding stereo pair.
To this end, we proceed in two steps: Firstly, building upon the recent RAFT-3D
approach, we develop an improved two-frame baseline by incorporating an
advanced stereo method. Secondly, and even more importantly, exploiting the
specific modeling concepts of RAFT-3D, we propose a U-Net architecture that
performs a fusion of forward and backward flow estimates and hence allows to
integrate temporal information on demand. Experiments on the KITTI benchmark do
not only show that the advantages of the improved baseline and the temporal
fusion approach complement each other, they also demonstrate that the computed
scene flow is highly accurate. More precisely, our approach ranks second
overall and first for the even more challenging foreground objects, in total
outperforming the original RAFT-3D method by more than 16%. Code is available
at https://github.com/cv-stuttgart/M-FUSE.
