论文标题
在无监督的光流中很重要
What Matters in Unsupervised Optical Flow
论文作者
论文摘要
我们系统地比较和分析了无监督的光流中的一组关键组件，以确定哪种光度损失，遮挡处理和平滑度正则化最有效。除了这项调查外，我们还为无监督的流量模型构建了许多新的改进，例如成本量归一化，在遮挡面罩处停止梯度，鼓励在提高流场之前的平滑度，并通过图像调整图像进行连续的自我审视。通过将调查的结果与改进的模型组件相结合，我们能够提出一种新的无监督流动技术，可以显着胜过以前无监督的最先进的最新技术，并在KITTI 2015数据集中与监督Flownet2的表现同在，同时也比相关方法更简单。
We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is most effective. Alongside this investigation we construct a number of novel improvements to unsupervised flow models, such as cost volume normalization, stopping the gradient at the occlusion mask, encouraging smoothness before upsampling the flow field, and continual self-supervision with image resizing. By combining the results of our investigation with our improved model components, we are able to present a new unsupervised flow technique that significantly outperforms the previous unsupervised state-of-the-art and performs on par with supervised FlowNet2 on the KITTI 2015 dataset, while also being significantly simpler than related approaches.
