Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of “events”. The innovative way they acquire data presents several advantages over standard devices, especially in poor lighting and high-speed motion conditions. However, the novelty of these sensors results in the lack of a large amount of training data capable of fully unlocking their potential. The most common approach implemented by researchers to address this issue is to leverage simulated event data . Yet, this approach comes with an open research question: how well simulated data generalize to real data? To answer this, we propose to exploit, in the event-based context, recent Domain Adaptation (DA) advances in traditional computer vision, showing that DA techniques applied to event data help reduce the sim-to-real gap. To this purpose, we propose a novel architecture, which we call Multi-View DA4E (MV-DA4E), that better exploits the peculiarities of frame-based event representations while also promoting domain invariant characteristics in features. Through extensive experiments, we prove the effectiveness of DA methods and MV-DA4E on N-Caltech101. Moreover, we validate their soundness in a real-world scenario through a cross-domain analysis on the popular RGB-D Object Dataset (ROD), which we extended to the event modality (RGB-E).

中文翻译：

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DA4Event：使用领域适应弥合事件相机的模拟到真实的差距

事件相机是新型的仿生传感器，它以“事件”的形式异步捕捉像素级强度变化。他们获取数据的创新方式与标准设备相比具有多项优势，尤其是在光线不足和高速运动条件下。然而，这些传感器的新颖性导致缺乏能够充分发挥其潜力的大量训练数据。研究人员为解决此问题而实施的最常见方法是利用模拟事件数据。然而，这种方法伴随着一个开放的研究问题：模拟数据如何泛化到真实数据？为了回答这个问题，我们建议在基于事件的上下文中利用传统计算机视觉领域最近领域适应 (DA) 的进步，表明应用于事件数据的 DA 技术有助于减少模拟到真实的差距。为此，我们提出了一种新颖的架构，我们称之为多视图 DA4E (MV-DA4E)，它更好地利用了基于帧的事件表示的特性，同时还促进了特征的域不变特性。通过大量实验，我们证明了 DA 方法和 MV-DA4E 在 N-Caltech101 上的有效性。此外，我们通过对流行的 RGB-D 对象数据集 (ROD) 进行跨域分析来验证它们在真实场景中的可靠性，我们将其扩展到事件模态 (RGB-E)。