  3D scene flow estimation from point clouds is a low-level 3D motion
perception task in computer vision. Flow embedding is a commonly used technique
in scene flow estimation, and it encodes the point motion between two
consecutive frames. Thus, it is critical for the flow embeddings to capture the
correct overall direction of the motion. However, previous works only search
locally to determine a soft correspondence, ignoring the distant points that
turn out to be the actual matching ones. In addition, the estimated
correspondence is usually from the forward direction of the adjacent point
clouds, and may not be consistent with the estimated correspondence acquired
from the backward direction. To tackle these problems, we propose a novel
all-to-all flow embedding layer with backward reliability validation during the
initial scene flow estimation. Besides, we investigate and compare several
design choices in key components of the 3D scene flow network, including the
point similarity calculation, input elements of predictor, and predictor &
refinement level design. After carefully choosing the most effective designs,
we are able to present a model that achieves the state-of-the-art performance
on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses
all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on
KITTI Scene Flow dataset for EPE3D metric. We release our codes at
https://github.com/IRMVLab/3DFlow.
