  Scene flow estimation is the task to predict the point-wise or pixel-wise 3D
displacement vector between two consecutive frames of point clouds or images,
which has important application in fields such as service robots and autonomous
driving. Although many previous works have explored greatly on scene flow
estimation based on point clouds, there are two problems that have not been
noticed or well solved before: 1) Points of adjacent frames in repetitive
patterns may be wrongly associated due to similar spatial structure in their
neighbourhoods; 2) Scene flow between adjacent frames of point clouds with
long-distance movement may be inaccurately estimated. To solve the first
problem, a novel context-aware set convolution layer is proposed in this paper
to exploit contextual structure information of Euclidean space and learn soft
aggregation weights for local point features. This design is inspired by human
perception of contextual structure information during scene understanding with
repetitive patterns. The context-aware set convolution layer is incorporated in
a context-aware point feature pyramid module of 3D point clouds for scene flow
estimation. For the second problem, an explicit residual flow learning
structure is proposed in the residual flow refinement layer to cope with
long-distance movement. The experiments and ablation study on FlyingThings3D
and KITTI scene flow datasets demonstrate the effectiveness of each proposed
component. The qualitative results show that the problems of ambiguous
inter-frame association and long-distance movement estimation are well handled.
Quantitative results on both FlyingThings3D and KITTI scene flow datasets show
that the proposed method achieves state-of-the-art performance, surpassing all
other previous works to the best of our knowledge by at least 25%.
