  Scene flow represents the 3D motion of each point in the scene, which
explicitly describes the distance and the direction of each point's movement.
Scene flow estimation is used in various applications such as autonomous
driving fields, activity recognition, and virtual reality fields. As it is
challenging to annotate scene flow with ground truth for real-world data, this
leaves no real-world dataset available to provide a large amount of data with
ground truth for scene flow estimation. Therefore, many works use synthesized
data to pre-train their network and real-world LiDAR data to finetune. Unlike
the previous unsupervised learning of scene flow in point clouds, we propose to
use odometry information to assist the unsupervised learning of scene flow and
use real-world LiDAR data to train our network. Supervised odometry provides
more accurate shared cost volume for scene flow. In addition, the proposed
network has mask-weighted warp layers to get a more accurate predicted point
cloud. The warp operation means applying an estimated pose transformation or
scene flow to a source point cloud to obtain a predicted point cloud and is the
key to refining scene flow from coarse to fine. When performing warp
operations, the points in different states use different weights for the pose
transformation and scene flow transformation. We classify the states of points
as static, dynamic, and occluded, where the static masks are used to divide
static and dynamic points, and the occlusion masks are used to divide occluded
points. The mask-weighted warp layer indicates that static masks and occlusion
masks are used as weights when performing warp operations. Our designs are
proved to be effective in ablation experiments. The experiment results show the
promising prospect of an odometry-assisted unsupervised learning method for 3D
scene flow in real-world data.
