  Predicting the future can significantly improve the safety of intelligent
vehicles, which is a key component in autonomous driving. 3D point clouds
accurately model 3D information of surrounding environment and are crucial for
intelligent vehicles to perceive the scene. Therefore, prediction of 3D point
clouds has great significance for intelligent vehicles, which can be utilized
for numerous further applications. However, due to point clouds are unordered
and unstructured, point cloud prediction is challenging and has not been deeply
explored in current literature. In this paper, we propose a novel motion-based
neural network named MoNet. The key idea of the proposed MoNet is to integrate
motion features between two consecutive point clouds into the prediction
pipeline. The introduction of motion features enables the model to more
accurately capture the variations of motion information across frames and thus
make better predictions for future motion. In addition, content features are
introduced to model the spatial content of individual point clouds. A recurrent
neural network named MotionRNN is proposed to capture the temporal correlations
of both features. Besides, we propose an attention-based motion align module to
address the problem of missing motion features in the inference pipeline.
Extensive experiments on two large scale outdoor LiDAR datasets demonstrate the
performance of the proposed MoNet. Moreover, we perform experiments on
applications using the predicted point clouds and the results indicate the
great application potential of the proposed method.
