Current deep visual local feature detectors do not model the spatial
uncertainty of detected features, producing suboptimal results in downstream
applications. In this work, we propose two post-hoc covariance estimates that
can be plugged into any pretrained deep feature detector: a simple, isotropic
covariance estimate that uses the predicted score at a given pixel location,
and a full covariance estimate via the local structure tensor of the learned
score maps. Both methods are easy to implement and can be applied to any deep
feature detector. We show that these covariances are directly related to errors
in feature matching, leading to improvements in downstream tasks, including
solving the perspective-n-point problem and motion-only bundle adjustment. Code
is available at https://github.com/javrtg/DA