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This paper proposes a self-supervised learned local detector and descriptor,called EventPoint, for event stream/camera tracking and registration.Event-based cameras have grown in popularity because of their biologicalinspiration and low power consumption. Despite this, applying local featuresdirectly to the event stream is difficult due to its peculiar data structure.We propose a new time-surface-like event stream representation method calledTencode. The event stream data processed by Tencode can obtain the pixel-levelpositioning of interest points while also simultaneously extracting descriptorsthrough a neural network. Instead of using costly and unreliable manualannotation, our network leverages the prior knowledge of local featureextraction on color images and conducts self-supervised learning viahomographic and spatio-temporal adaptation. To the best of our knowledge, ourproposed method is the first research on event-based local features learningusing a deep neural network. We provide comprehensive experiments of featurepoint detection and matching, and three public datasets are used for evaluation(i.e. DSEC, N-Caltech101, and HVGA ATIS Corner Dataset). The experimentalfindings demonstrate that our method outperforms SOTA in terms of feature pointdetection and description.

EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera