DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation

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 extit{simulated event data}. Yet, this approach comes with an open research question: extit{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 extit{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).