Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds for large-scale point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feed forward methods are considerably faster, running on the order of tens to hundreds of milliseconds for large-scale point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our instantiation of this framework, ZeroFlow, produces scene flow estimates in real-time on large-scale point clouds at quality competitive with state-of-the-art methods while using zero human labels. Notably, at test-time ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art optimization-based methods on large-scale point clouds and over 1000$\times$ cheaper to train on unlabeled data compared to the cost of human annotation of that data. To facilitate research reuse, we release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.

翻译：场景流估计是描述时间连续点云之间三维运动场的任务。现有最先进方法虽然利用强先验知识和测试时优化技术，但在大规模点云上需耗时数十秒，无法作为开放世界目标检测等实时应用的计算机视觉基础模块。前馈方法速度显著提升，处理大规模点云仅需数十至数百毫秒，但需要昂贵的人工标注。为解决这两方面局限性，我们提出基于蒸馏的场景流估计，该简易蒸馏框架采用无标签优化方法生成伪标签以监督前馈模型。该框架的实例化版本ZeroFlow可在大规模点云上实现实时场景流估计，其质量与现有最先进方法相当，且完全无需人工标注。值得注意的是，在测试阶段，ZeroFlow在大规模点云上的处理速度比基于优化的无标签最先进方法快1000倍以上；同时在无标签数据上的训练成本比人工标注数据降低1000倍以上。为促进研究复用，我们开源了代码、预训练模型权重，并提供了针对Argoverse 2和Waymo Open数据集的高质量伪标签。