  Point cloud scene flow estimation is of practical importance for dynamic
scene navigation in autonomous driving. Since scene flow labels are hard to
obtain, current methods train their models on synthetic data and transfer them
to real scenes. However, large disparities between existing synthetic datasets
and real scenes lead to poor model transfer. We make two major contributions to
address that. First, we develop a point cloud collector and scene flow
annotator for GTA-V engine to automatically obtain diverse realistic training
samples without human intervention. With that, we develop a large-scale
synthetic scene flow dataset GTA-SF. Second, we propose a mean-teacher-based
domain adaptation framework that leverages self-generated pseudo-labels of the
target domain. It also explicitly incorporates shape deformation regularization
and surface correspondence refinement to address distortions and misalignments
in domain transfer. Through extensive experiments, we show that our GTA-SF
dataset leads to a consistent boost in model generalization to three real
datasets (i.e., Waymo, Lyft and KITTI) as compared to the most widely used FT3D
dataset. Moreover, our framework achieves superior adaptation performance on
six source-target dataset pairs, remarkably closing the average domain gap by
60%. Data and codes are available at https://github.com/leolyj/DCA-SRSFE
