  Scene flow represents the motion information of each point in the 3D point
clouds. It is a vital downstream method applied to many tasks, such as motion
segmentation and object tracking. However, there are always occlusion points
between two consecutive point clouds, whether from the sparsity data sampling
or real-world occlusion. In this paper, we focus on addressing occlusion issues
in scene flow by the semantic self-similarity and motion consistency of the
moving objects. We propose a GMA3D module based on the transformer framework,
which utilizes local and global semantic similarity to infer the motion
information of occluded points from the motion information of local and global
non-occluded points respectively, and then uses an offset aggregator to
aggregate them. Our module is the first to apply the transformer-based
architecture to gauge the scene flow occlusion problem on point clouds.
Experiments show that our GMA3D can solve the occlusion problem in the scene
flow, especially in the real scene. We evaluated the proposed method on the
occluded version of point cloud datasets and get state-of-the-art results on
the real scene KITTI dataset. To testify that GMA3D is still beneficial to
non-occluded scene flow, we also conducted experiments on non-occluded version
datasets and achieved promising performance on FlyThings3D and KITTI. The code
is available at https://anonymous.4open.science/r/GMA3D-E100.
