  Predicting future 3D LiDAR pointclouds is a challenging task that is useful
in many applications in autonomous driving such as trajectory prediction, pose
forecasting and decision making. In this work, we propose a new LiDAR
prediction framework that is based on generative models namely Variational
Recurrent Neural Networks (VRNNs), titled Stochastic LiDAR Prediction and
Completion (SLPC). Our algorithm is able to address the limitations of previous
video prediction frameworks when dealing with sparse data by spatially
inpainting the depth maps in the upcoming frames. Our contributions can thus be
summarized as follows: we introduce the new task of predicting and completing
depth maps from spatially sparse data, we present a sparse version of VRNNs and
an effective self-supervised training method that does not require any labels.
Experimental results illustrate the effectiveness of our framework in
comparison to the state of the art methods in video prediction.
