GERD: Geometric event response data generation

• URL: http://arxiv.org/abs/2412.03259v1
• Date: Wed, 04 Dec 2024 11:59:36 GMT
• Title: GERD: Geometric event response data generation
• Authors: Jens Egholm Pedersen, Dimitris Korakovounis, Jörg Conradt,
• Abstract summary: Event-based vision sensors are appealing because of their time resolution, higher dynamic range, and low-power consumption.<n>They also provide data that is fundamentally different from conventional frame-based cameras: events are sparse, discrete, and require integration in time.<n>We introduce a method to generate event-based data under controlled transformations.
• Score: 1.5269221584932013
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Event-based vision sensors are appealing because of their time resolution, higher dynamic range, and low-power consumption. They also provide data that is fundamentally different from conventional frame-based cameras: events are sparse, discrete, and require integration in time. Unlike conventional models grounded in established geometric and physical principles, event-based models lack comparable foundations. We introduce a method to generate event-based data under controlled transformations. Specifically, we subject a prototypical object to transformations that change over time to produce carefully curated event videos. We hope this work simplifies studies for geometric approaches in event-based vision. GERD is available at https://github.com/ncskth/gerd

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