  Scene flow estimation, which aims to predict per-point 3D displacements of
dynamic scenes, is a fundamental task in the computer vision field. However,
previous works commonly suffer from unreliable correlation caused by locally
constrained searching ranges, and struggle with accumulated inaccuracy arising
from the coarse-to-fine structure. To alleviate these problems, we propose a
novel uncertainty-aware scene flow estimation network (DifFlow3D) with the
diffusion probabilistic model. Iterative diffusion-based refinement is designed
to enhance the correlation robustness and resilience to challenging cases, e.g.
dynamics, noisy inputs, repetitive patterns, etc. To restrain the generation
diversity, three key flow-related features are leveraged as conditions in our
diffusion model. Furthermore, we also develop an uncertainty estimation module
within diffusion to evaluate the reliability of estimated scene flow. Our
DifFlow3D achieves state-of-the-art performance, with 24.0% and 29.1% EPE3D
reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably, our
method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D)
on the KITTI dataset. Additionally, our diffusion-based refinement paradigm can
be readily integrated as a plug-and-play module into existing scene flow
networks, significantly increasing their estimation accuracy. Codes are
released at https://github.com/IRMVLab/DifFlow3D.
