  Anticipating the future in a dynamic scene is critical for many fields such
as autonomous driving and robotics. In this paper we propose a class of novel
neural network architectures to predict future LiDAR frames given previous
ones. Since the ground truth in this application is simply the next frame in
the sequence, we can train our models in a self-supervised fashion. Our
proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use
Chamfer Distance (CD) and Earth Mover's Distance (EMD) as loss functions and
evaluation metrics. We train and evaluate our models using the newly released
nuScenes dataset, and characterize their performance and complexity with
several baselines. Compared to directly using FlowNet3D, our proposed
architectures achieve CD and EMD nearly an order of magnitude lower. In
addition, we show that our predictions generate reasonable scene flow
approximations without using any labelled supervision.
