  Scene flow estimation, which extracts point-wise motion between scenes, is
becoming a crucial task in many computer vision tasks. However, all of the
existing estimation methods utilize only the unidirectional features,
restricting the accuracy and generality. This paper presents a novel scene flow
estimation architecture using bidirectional flow embedding layers. The proposed
bidirectional layer learns features along both forward and backward directions,
enhancing the estimation performance. In addition, hierarchical feature
extraction and warping improve the performance and reduce computational
overhead. Experimental results show that the proposed architecture achieved a
new state-of-the-art record by outperforming other approaches with large margin
in both FlyingThings3D and KITTI benchmarks. Codes are available at
https://github.com/cwc1260/BiFlow.
