Neural Scene Flow Prior
We propose a neural scene flow prior that
utilizes the architecture of neural networks (MLPs) as an 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.
Also, the implicit and continuous scene flow representation allows us to estimate
dense long-term correspondences across a sequence of point clouds.