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.

本文提出了一种新颖的端对端深度场景流模型PointPWC-Net，通过粗到细的方式处理3D点云数据，包括新型的成本体量、上采样和变形层来有效地处理3D点云数据，利用自监督损失进行训练。实验结果表明，在FlyingThings3D数据集上超过了现有技术，并且在未经微调的情况下，在KITTI Scene Flow 2015数据集上表现出很好的泛化能力，优于所有先前的方法。

PointPWC-Net: 一种基于粗到细的网络，用于3D点云的监督学习与自监督学习场景流估计