Self-supervised 3D point cloud prediction for moving objects based on deep correlation information
This work reformulate point cloud prediction as a unified spatio-temporal correlation learning problem and proposes a novel framework, the Motion-guided Spatio-Temporal Point Cloud Prediction Network (MoCNet), which achieves higher prediction accuracy and better generalization ability.