用潜在空间距离评估事件相机数据仿真质量，提升真实感。
Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space
- 基于RVT模型激活值计算潜在空间距离，量化仿真数据真实度。
- 在DSEC数据集上，EQS越高，模型在真实数据上泛化能力越强。
- 适合做事件相机仿真器优化的研究者和开发者使用。
事件相机凭借低延迟、高动态范围和异步事件特性，有望革新视觉感知。然而，高质量标注数据集稀缺阻碍了其在深度学习驱动的计算机视觉中的广泛应用。为缓解此问题，已有多种模拟器用于生成合成事件数据以训练检测与估计模型。但事件相机与传统帧相机在传感器设计上存在根本差异，导致现有仿真难以真实还原实际采集数据。受图像比较中深度特征应用的启发，本文提出事件质量评分（EQS），利用RVT架构的激活值计算潜在空间距离。在DSEC驾驶数据集上的仿真到真实实验表明，更高的EQS意味着在真实数据上具有更好的泛化性能。因此，以EQS为目标优化可构建更真实的事件相机仿真器，有效缩小仿真差距。代码已开源：https://github.com/eventbasedvision/EQS。
原文摘要 · Abstract (English)
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.
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