  Weakly supervised learning can help local feature methods to overcome the
obstacle of acquiring a large-scale dataset with densely labeled
correspondences. However, since weak supervision cannot distinguish the losses
caused by the detection and description steps, directly conducting weakly
supervised learning within a joint describe-then-detect pipeline suffers
limited performance. In this paper, we propose a decoupled describe-then-detect
pipeline tailored for weakly supervised local feature learning. Within our
pipeline, the detection step is decoupled from the description step and
postponed until discriminative and robust descriptors are learned. In addition,
we introduce a line-to-window search strategy to explicitly use the camera pose
information for better descriptor learning. Extensive experiments show that our
method, namely PoSFeat (Camera Pose Supervised Feature), outperforms previous
fully and weakly supervised methods and achieves state-of-the-art performance
on a wide range of downstream tasks.
