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

DAC: Detector-Agnostic Spatial Covariances for Deep Local Features