  Event cameras, drawing inspiration from biological systems, efficiently
detect changes in ambient light with low latency and high dynamic range while
consuming minimal power. The most current approach to processing event data
often involves converting it into frame-based representations, which is
well-established in traditional vision. However, this approach neglects the
sparsity of event data, loses fine-grained temporal information during the
transformation process, and increases the computational burden, making it
ineffective for characterizing event camera properties. In contrast, Point
Cloud is a popular representation for 3D processing and is better suited to
match the sparse and asynchronous nature of the event camera. Nevertheless,
despite the theoretical compatibility of point-based methods with event
cameras, the results show a performance gap that is not yet satisfactory
compared to frame-based methods. In order to bridge the performance gap, we
propose EventMamba, an efficient and effective Point Cloud framework that
achieves competitive results even compared to the state-of-the-art (SOTA)
frame-based method in both classification and regression tasks. This notable
accomplishment is facilitated by our rethinking of the distinction between
Event Cloud and Point Cloud, emphasizing effective temporal information
extraction through optimized network structures. Specifically, EventMamba
leverages temporal aggregation and State Space Model (SSM) based Mamba boasting
enhanced temporal information extraction capabilities. Through a hierarchical
structure, EventMamba is adept at abstracting local and global spatial features
and implicit and explicit temporal features. By adhering to the lightweight
design principle, EventMamba delivers impressive results with minimal
computational resource utilization, demonstrating its efficiency and
effectiveness.
