This repository implements FlowNet3D, a deep learning model published at CVPR 2019, designed to estimate scene flow directly from 3D point clouds. The network learns hierarchical features of point clouds and flow embeddings representing point motions through novel learning layers for point sets. It is trained on synthetic FlyingThings3D data and evaluated on KITTI Lidar scans, demonstrating generalization to real-world data.