  We tackle the problem of estimating flow between two images with large
lighting variations. Recent learning-based flow estimation frameworks have
shown remarkable performance on image pairs with small displacement and
constant illuminations, but cannot work well on cases with large viewpoint
change and lighting variations because of the lack of pixel-wise flow
annotations for such cases. We observe that via the Structure-from-Motion (SfM)
techniques, one can easily estimate relative camera poses between image pairs
with large viewpoint change and lighting variations. We propose a novel weakly
supervised framework LIFE to train a neural network for estimating accurate
lighting-invariant flows between image pairs. Sparse correspondences are
conventionally established via feature matching with descriptors encoding local
image contents. However, local image contents are inevitably ambiguous and
error-prone during the cross-image feature matching process, which hinders
downstream tasks. We propose to guide feature matching with the flows predicted
by LIFE, which addresses the ambiguous matching by utilizing abundant context
information in the image pairs. We show that LIFE outperforms previous flow
learning frameworks by large margins in challenging scenarios, consistently
improves feature matching, and benefits downstream tasks.
