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We address the problem of scene flow: given a pair of stereo or RGB-D videoframes, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deeparchitecture for scene flow. RAFT-3D is based on the RAFT model developed foroptical flow but iteratively updates a dense field of pixelwise SE3 motioninstead of 2D motion. A key innovation of RAFT-3D is rigid-motion embeddings,which represent a soft grouping of pixels into rigid objects. Integral torigid-motion embeddings is Dense-SE3, a differentiable layer that enforcesgeometric consistency of the embeddings. Experiments show that RAFT-3D achievesstate-of-the-art performance. On FlyingThings3D, under the two-view evaluation,we improved the best published accuracy (d < 0.05) from 34.3% to 83.7%. OnKITTI, we achieve an error of 5.77, outperforming the best published method(6.31), despite using no object instance supervision. Code is available athttps://github.com/princeton-vl/RAFT-3D.

RAFT-3D: Scene Flow using Rigid-Motion Embeddings