  Sparse local feature extraction is usually believed to be of important
significance in typical vision tasks such as simultaneous localization and
mapping, image matching and 3D reconstruction. At present, it still has some
deficiencies needing further improvement, mainly including the discrimination
power of extracted local descriptors, the localization accuracy of detected
keypoints, and the efficiency of local feature learning. This paper focuses on
promoting the currently popular sparse local feature learning with camera pose
supervision. Therefore, it pertinently proposes a Shared Coupling-bridge scheme
with four light-weight yet effective improvements for weakly-supervised local
feature (SCFeat) learning. It mainly contains: i) the
\emph{Feature-Fusion-ResUNet Backbone} (F2R-Backbone) for local descriptors
learning, ii) a shared coupling-bridge normalization to improve the decoupling
training of description network and detection network, iii) an improved
detection network with peakiness measurement to detect keypoints and iv) the
fundamental matrix error as a reward factor to further optimize feature
detection training. Extensive experiments prove that our SCFeat improvement is
effective. It could often obtain a state-of-the-art performance on classic
image matching and visual localization. In terms of 3D reconstruction, it could
still achieve competitive results. For sharing and communication, our source
codes are available at https://github.com/sunjiayuanro/SCFeat.git.
