Current scene flow methods broadly fail to describe motion on small objects,
and current scene flow evaluation protocols hide this failure by averaging over
many points, with most drawn larger objects. To fix this evaluation failure, we
propose a new evaluation protocol, Bucket Normalized EPE, which is class-aware
and speed-normalized, enabling contextualized error comparisons between object
types that move at vastly different speeds. To highlight current method
failures, we propose a frustratingly simple supervised scene flow baseline,
TrackFlow, built by bolting a high-quality pretrained detector (trained using
many class rebalancing techniques) onto a simple tracker, that produces
state-of-the-art performance on current standard evaluations and large
improvements over prior art on our new evaluation. Our results make it clear
that all scene flow evaluations must be class and speed aware, and supervised
scene flow methods must address point class imbalances. We release the
evaluation code publicly at
this https URL

现有的场景流方法普遍无法描述小物体的运动，而当前的场景流评估协议通过对许多点进行平均处理来隐藏这种失败，我们提出了一种新的评估协议，Bucket Normalized EPE，它是类感知的且速度标准化的，可以在移动速度迥异的不同对象类型之间进行情境化的错误比较，为了凸显当前方法的失败，我们提出了一种令人沮丧地简单的有监督场景流基线，TrackFlow，通过将高质量预训练检测器（使用多种类平衡技术进行训练）融合到简单的追踪器上，产生了在当前标准评估上的最新性能并在我们的新评估中相对于先前的技术有了显著的改进，我们的结果表明，所有的场景流评估都必须具备类和速度的意识，有监督的场景流方法必须解决点类不平衡的问题，我们公开发布了评估代码在指定的 URL 链接中。