GERD: Geometric event response data generation

Jens Egholm Pedersen; Dimitris Korakovounis; Jörg Conradt

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arXiv · 2412.03259

GERD: Geometric event response data generation

Jens Egholm Pedersen·Dimitris Korakovounis·Jörg Conradt

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Abstract

Event-based vision sensors offer high temporal resolution, high dynamic range, and low power consumption, yet event-based vision models lag behind conventional frame-based vision methods. We argue that this gap is partly due to the lack of principled study of the transformation groups that govern event-based visual streams. Motivated by the role that geometric and group-theoretic methods have played in advancing computer vision, we present GERD: a simulator for generating event-based recordings of objects under precisely controlled affine, Galilean, and temporal scaling transformations. By providing ground-truth transformations at each timestep, GERD enables hypothesis-driven and controlled studies of geometric properties that are otherwise hard to isolate in real-world datasets or with current event simulators. GERD supports three noise models and sub-pixel motion as a complement to real sensor datasets. We demonstrate its use in training by evaluating models from the literature with geometric guarantees and release GERD as an open tool available at

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Jens Egholm Pedersen, Dimitris Korakovounis, Jörg Conradt. 2024-12-04. GERD: Geometric event response data generation. https://arxiv.org/abs/2412.03259

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