  This work focuses on mitigating two limitations in the joint learning of
local feature detectors and descriptors. First, the ability to estimate the
local shape (scale, orientation, etc.) of feature points is often neglected
during dense feature extraction, while the shape-awareness is crucial to
acquire stronger geometric invariance. Second, the localization accuracy of
detected keypoints is not sufficient to reliably recover camera geometry, which
has become the bottleneck in tasks such as 3D reconstruction. In this paper, we
present ASLFeat, with three light-weight yet effective modifications to
mitigate above issues. First, we resort to deformable convolutional networks to
densely estimate and apply local transformation. Second, we take advantage of
the inherent feature hierarchy to restore spatial resolution and low-level
details for accurate keypoint localization. Finally, we use a peakiness
measurement to relate feature responses and derive more indicative detection
scores. The effect of each modification is thoroughly studied, and the
evaluation is extensively conducted across a variety of practical scenarios.
State-of-the-art results are reported that demonstrate the superiority of our
methods.
