  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 biological
inspiration and low power consumption. Despite this, applying local features
directly to the event stream is difficult due to its peculiar data structure.
We propose a new time-surface-like event stream representation method called
Tencode. The event stream data processed by Tencode can obtain the pixel-level
positioning of interest points while also simultaneously extracting descriptors
through a neural network. Instead of using costly and unreliable manual
annotation, our network leverages the prior knowledge of local feature
extraction on color images and conducts self-supervised learning via
homographic and spatio-temporal adaptation. To the best of our knowledge, our
proposed method is the first research on event-based local features learning
using a deep neural network. We provide comprehensive experiments of feature
point detection and matching, and three public datasets are used for evaluation
(i.e. DSEC, N-Caltech101, and HVGA ATIS Corner Dataset). The experimental
findings demonstrate that our method outperforms SOTA in terms of feature point
detection and description.
