SuperPoint Overview The SuperPoint model was proposed in SuperPoint: Self Supervised Interest Point Detection and Description by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. This model is the result of a self supervised training of a fully convolutional network for interest point detection and description. The model is able to detect interest points that are repeatable under homographic transformations and provide a descriptor for each point. The use of the model in its own is limited, but it can be used as a feature extractor for other tasks such as homography estimation, image matching, etc. The abstract from the paper is the following: This paper presents a self supervised framework for training interest point detectors and descriptors suitable for a large number of multiple view geometry problems in computer vision. As opposed to patch based neural networks, our fully convolutional model operates on full sized images and jointly computes pixel level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi scale, multi homography approach for boosting interest point detection repeatability and performing cr…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy