  Neural Scene Flow Prior (NSFP) is of significant interest to the vision
community due to its inherent robustness to out-of-distribution (OOD) effects
and its ability to deal with dense lidar points. The approach utilizes a
coordinate neural network to estimate scene flow at runtime, without any
training. However, it is up to 100 times slower than current state-of-the-art
learning methods. In other applications such as image, video, and radiance
function reconstruction innovations in speeding up the runtime performance of
coordinate networks have centered upon architectural changes. In this paper, we
demonstrate that scene flow is different -- with the dominant computational
bottleneck stemming from the loss function itself (i.e., Chamfer distance).
Further, we rediscover the distance transform (DT) as an efficient,
correspondence-free loss function that dramatically speeds up the runtime
optimization. Our fast neural scene flow (FNSF) approach reports for the first
time real-time performance comparable to learning methods, without any training
or OOD bias on two of the largest open autonomous driving (AV) lidar datasets
Waymo Open and Argoverse.
