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Scene flow in 3D point clouds plays an important role in understandingdynamic environments. Although significant advances have been made by deepneural networks, the performance is far from satisfactory as only per-pointtranslational motion is considered, neglecting the constraints of the rigidmotion in local regions. To address the issue, we propose to introduce themotion consistency to force the smoothness among neighboring points. Inaddition, constraints on the rigidity of the local transformation are alsoadded by sharing unique rigid motion parameters for all points within eachlocal region. To this end, a high-order CRFs based relation module (Con-HCRFs)is deployed to explore both point-wise smoothness and region-wise rigidity. Toempower the CRFs to have a discriminative unary term, we also introduce aposition-aware flow estimation module to be incorporated into the Con-HCRFs.Comprehensive experiments on FlyingThings3D and KITTI show that our proposedframework (HCRF-Flow) achieves state-of-the-art performance and significantlyoutperforms previous approaches substantially.

HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding