| Event-based vision sensors such as the DVS346 capture asynchronous streams of pixel-level brightness changes with ultrahigh temporal resolution and sparse representation, which makes them ideal for dynamic perception tasks. However, their output format is not inherently compatible with spiking neural networks (SNNs), which require structured spike train input to utilize temporal coding. In this ongoing work, we present a robust conversion framework that algorithmically transforms raw event streams collected from a DVS346 camera mounted on a Broxter robotic platform into spike train representations suitable for SNN processing. Our method preserves critical temporal dynamics from the original event data while enabling compatibility with neuromorphic learning paradigms. To facilitate model interpretation and validation, we also introduce a visualization suite that reconstructs both event and generated spike datasets into frame-based videos for qualitative analysis. The resulting dataset bridges the gap between neuromorphic sensing and computation, promoting efficient learning and real-time inference in SNN models, and advancing the integration of event-based vision into embodied robotic systems. |
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