Scene flow estimation is the task of describing the 3D motion field between
temporally successive point clouds. State-of-the-art methods use strong priors
and test-time optimization techniques, but require on the order of tens of
seconds for large-scale point clouds, making them unusable as computer vision
primitives for real-time applications such as open world object detection. Feed
forward methods are considerably faster, running on the order of tens to
hundreds of milliseconds for large-scale point clouds, but require expensive
human supervision. To address both limitations, we propose Scene Flow via
Distillation, a simple distillation framework that uses a label-free
optimization method to produce pseudo-labels to supervise a feed forward model.
Our instantiation of this framework, ZeroFlow, produces scene flow estimates in
real-time on large-scale point clouds at quality competitive with
state-of-the-art methods while using zero human labels. Notably, at test-time
ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art
optimization-based methods on large-scale point clouds and over 1000$\times$
cheaper to train on unlabeled data compared to the cost of human annotation of
that data. To facilitate research reuse, we release our code, trained model
weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open
datasets.

通过使用无需标签的方法来监督前馈模型，本文提出了一个简单的蒸馏框架，其实例 ZeroFlow 可以在大规模点云上实时进行场景流估计，而不需要人工标注。