Recent work has shown that optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make the network estimate its local uncertainty about the correctness of its prediction, which is vital information when building decisions on top of the estimated optical flow. 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 that enforces complementary hypotheses and provides uncertainty estimates efficiently within a single forward pass without the need for sampling or ensembles. We demonstrate high-quality uncertainty estimates that clearly improve over previous confidence measures on optical flow and allow for interactive frame rates.

本文旨在研究一种基于端到端的监督学习方法，用于提高光流估计领域中的准确性和实时性，并首次比较了多种策略和技术来准确估计局部不确定性，同时介绍了一种新的使用Winner-Takes-All loss的网络架构，显示它可以有效地提供互补的假设和不确定性估计，并且质量明显高于以往的光流置信度测量。

光流的不确定性估计和多假设网络