Abstract. Optical flow estimation can be formulated as an end-toend supervised learning problem, which yields estimates with a superior
accuracy-runtime tradeoff compared to alternative methodology. In this
paper, we make such networks estimate their local uncertainty about the
correctness of their prediction, which is vital information when building
decisions on top of the estimations. For the first time we compare several
strategies and techniques to estimate uncertainty in a large-scale computer vision task like optical flow estimation. Moreover, we introduce a
new network architecture and loss function that enforce complementary
hypotheses and provide uncertainty estimates efficiently with a single forward pass and without the need for sampling or ensembles. We demonstrate the quality of the uncertainty estimates, which is clearly above
previous confidence measures on optical flow and allows for interactive
frame rates