  We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on
3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level
is upsampled and warped to a finer level, enabling the algorithm to accommodate
for large motion without a prohibitive search space. We introduce novel cost
volume, upsampling, and warping layers to efficiently handle 3D point cloud
data. Unlike traditional cost volumes that require exhaustively computing all
the cost values on a high-dimensional grid, our point-based formulation
discretizes the cost volume onto input 3D points, and a PointConv operation
efficiently computes convolutions on the cost volume. Experiment results on
FlyingThings3D outperform the state-of-the-art by a large margin. We further
explore novel self-supervised losses to train our model and achieve comparable
results to state-of-the-art trained with supervised loss. Without any
fine-tuning, our method also shows great generalization ability on KITTI Scene
Flow 2015 dataset, outperforming all previous methods.
