  Before the deep learning revolution, many perception algorithms were based on
runtime optimization in conjunction with a strong prior/regularization penalty.
A prime example of this in computer vision is optical and scene flow.
Supervised learning has largely displaced the need for explicit regularization.
Instead, they rely on large amounts of labeled data to capture prior
statistics, which are not always readily available for many problems. Although
optimization is employed to learn the neural network, the weights of this
network are frozen at runtime. As a result, these learning solutions are
domain-specific and do not generalize well to other statistically different
scenarios. This paper revisits the scene flow problem that relies predominantly
on runtime optimization and strong regularization. A central innovation here is
the inclusion of a neural scene flow prior, which uses the architecture of
neural networks as a new type of implicit regularizer. Unlike learning-based
scene flow methods, optimization occurs at runtime, and our approach needs no
offline datasets -- making it ideal for deployment in new environments such as
autonomous driving. We show that an architecture based exclusively on
multilayer perceptrons (MLPs) can be used as a scene flow prior. Our method
attains competitive -- if not better -- results on scene flow benchmarks. Also,
our neural prior's implicit and continuous scene flow representation allows us
to estimate dense long-term correspondences across a sequence of point clouds.
The dense motion information is represented by scene flow fields where points
can be propagated through time by integrating motion vectors. We demonstrate
such a capability by accumulating a sequence of lidar point clouds.
