EventPoint: Self-Supervised Local Descriptor Learning for Event Cameras

• URL: http://arxiv.org/abs/2109.00210v1
• Date: Wed, 1 Sep 2021 06:58:14 GMT
• Title: EventPoint: Self-Supervised Local Descriptor Learning for Event Cameras
• Authors: Ze Huang, Songzhi Su, Henry Zhang, Kevin Sun
• Abstract summary: We propose a method of extracting intrest points and descriptors using self-supervised learning method on frame-based event data, which is called EventPoint. We train our model on real event-form driving dataset--DSEC with the self-supervised learning method we proposed, the training progress fully consider the characteristics of event data.
• Score: 2.3300629798610446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We proposes a method of extracting intrest points and descriptors using self-supervised learning method on frame-based event data, which is called EventPoint. Different from other feature extraction methods on event data, we train our model on real event-form driving dataset--DSEC with the self-supervised learning method we proposed, the training progress fully consider the characteristics of event data.To verify the effectiveness of our work,we conducted several complete evaluations: we emulated DART and carried out feature matching experiments on N-caltech101 dataset, the results shows that the effect of EventPoint is better than DART; We use Vid2e tool provided by UZH to convert Oxford robotcar data into event-based format, and combined with INS information provided to carry out the global pose estimation experiment which is important in SLAM. As far as we know, this is the first work to carry out this challenging task.Sufficient experimental data show that EventPoint can get better results while achieve real time on CPU.

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