Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
Event cameras promise a paradigm shift in vision sensing with their low
latency, high dynamic range, and asynchronous nature of events. Unfortunately,
the scarcity of high-quality labeled datasets hinders their widespread adoption
in deep learning-driven computer vision. To mitigate this, several simulators
have been proposed to generate synthetic event data for training models for
detection and estimation tasks. However, the fundamentally different sensor
design of event cameras compared to traditional frame-based cameras poses a
challenge for accurate simulation. As a result, most simulated data fail to
mimic data captured by real event cameras. Inspired by existing work on using
deep features for image comparison, we introduce event quality score (EQS), a
quality metric that utilizes activations of the RVT architecture. Through
sim-to-real experiments on the DSEC driving dataset, it is shown that a higher
EQS implies improved generalization to real-world data after training on
simulated events. Thus, optimizing for EQS can lead to developing more
realistic event camera simulators, effectively reducing the simulation gap. EQS
is available at https://github.com/eventbasedvision/EQS.
