  Scene flow prediction is a crucial underlying task in understanding dynamic
scenes as it offers fundamental motion information. However, contemporary scene
flow methods encounter three major challenges. Firstly, flow estimation solely
based on local receptive fields lacks long-dependency matching of point pairs.
To address this issue, we propose global attentive flow embedding to match
all-to-all point pairs in both feature space and Euclidean space, providing
global initialization before local refinement. Secondly, there are deformations
existing in non-rigid objects after warping, which leads to variations in the
spatiotemporal relation between the consecutive frames. For a more precise
estimation of residual flow, a spatial temporal feature re-embedding module is
devised to acquire the sequence features after deformation. Furthermore,
previous methods perform poor generalization due to the significant domain gap
between the synthesized and LiDAR-scanned datasets. We leverage novel domain
adaptive losses to effectively bridge the gap of motion inference from
synthetic to real-world. Experiments demonstrate that our approach achieves
state-of-the-art performance across various datasets, with particularly
outstanding results on real-world LiDAR-scanned datasets. Our code is available
at https://github.com/O-VIGIA/StarFlow.
