High-efficiency sparse convolution operator for event-based cameras

TL;DR

This paper introduces a new convolution operator that makes processing event-based camera data faster and more efficient.

Contribution

A novel sparse convolution operator that significantly reduces computation for event-based cameras without losing accuracy.

Findings

The sparse convolution operator reduces computational workload by nearly 90%.

It achieves almost 2× acceleration in processing speed while maintaining accuracy.

The method enables low-latency perception for robotic applications using event-based cameras.

Abstract

Event-based cameras are bio-inspired vision sensors that mimic the sparse and asynchronous activation of the animal retina, offering advantages such as low latency and low computational load in various robotic applications. However, despite their inherent sparsity, most existing visual processing algorithms are optimized for conventional standard cameras and dense images captured from them, resulting in computational redundancy and high latency when applied to event-based cameras. To address this gap, we propose a sparse convolution operator tailored for event-based cameras. By selectively skipping invalid sub-convolutions and efficiently reorganizing valid computations, our operator reduces computational workload by nearly 90% and achieves almost 2× acceleration in processing speed, while maintaining the same accuracy as dense convolution operators. This innovation unlocks the potential of event-based cameras in applications such as autonomous navigation, real-time object tracking, and industrial inspection, enabling low-latency and high-efficiency perception in resource-constrained robotic systems.

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