DAC: Detector-Agnostic Spatial Covariances for Deep Local Features

• URL: http://arxiv.org/abs/2305.12250v2
• Date: Tue, 15 Aug 2023 14:13:22 GMT
• Title: DAC: Detector-Agnostic Spatial Covariances for Deep Local Features
• Authors: Javier Tirado-Gar\'in, Frederik Warburg, Javier Civera
• Abstract summary: Current deep visual local feature detectors do not model the spatial uncertainty of detected features. We propose two post-hoc covariance estimates that can be plugged into any pretrained deep feature detector.
• Score: 11.494662473750505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/DAC

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