We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.

我们提出了一种新的基于CNN的算法来对多帧光流进行估计。算法引入了一个名为STaR的细胞，通过对空间尺度和时间进行双重递归，包括了一种基于传递学习特征而非光流估计的时间递归和一个与光流估计耦合的遮挡检测过程，这使用了极少的额外参数。这些结果表明，STaRFlow算法在MPI Sintel和Kitti2015上具有最先进的性能，并且涉及的参数显著比所有其他具有可比结果的方法都要少。

STaRFlow：一种轻量级多帧光流估计的时空循环单元