  Scene flow depicts the dynamics of a 3D scene, which is critical for various
applications such as autonomous driving, robot navigation, AR/VR, etc.
Conventionally, scene flow is estimated from dense/regular RGB video frames.
With the development of depth-sensing technologies, precise 3D measurements are
available via point clouds which have sparked new research in 3D scene flow.
Nevertheless, it remains challenging to extract scene flow from point clouds
due to the sparsity and irregularity in typical point cloud sampling patterns.
One major issue related to irregular sampling is identified as the randomness
during point set abstraction/feature extraction -- an elementary process in
many flow estimation scenarios. A novel Spatial Abstraction with Attention
(SA^2) layer is accordingly proposed to alleviate the unstable abstraction
problem. Moreover, a Temporal Abstraction with Attention (TA^2) layer is
proposed to rectify attention in temporal domain, leading to benefits with
motions scaled in a larger range. Extensive analysis and experiments verified
the motivation and significant performance gains of our method, dubbed as Flow
Estimation via Spatial-Temporal Attention (FESTA), when compared to several
state-of-the-art benchmarks of scene flow estimation.
