Abstract
Neuromorphic, or event, cameras represent a transformation in the classical approach to visual sensing encodes detected instantaneous per-pixel illumination changes into an asynchronous stream of event packets. Their novelty compared to standard cameras lies in the transition from capturing full picture frames at fixed time intervals to a sparse data format which, with its distinctive qualities, offers potential improvements in various applications. However, these advantages come at the cost of reinventing algorithmic procedures or adapting them to effectively process the new data format. In this survey, we systematically examine neuromorphic vision along three main dimensions. First, we highlight the technological evolution and distinctive hardware features of neuromorphic cameras from their inception to recent models. Second, we review image processing algorithms developed explicitly for event-based data, covering key works on feature detection, tracking, and optical flow -which form the basis for analyzing image elements and transformations -as well as depth and pose estimation or object recognition, which interpret more complex scene structures and components. These techniques, drawn from classical computer vision and modern data-driven approaches, are examined to illustrate the breadth of applications for event-based cameras. Third, we present practical application case studies demonstrating how event cameras have been successfully used across various industries and scenarios. Finally, we analyze the challenges limiting widespread adoption, identify significant research gaps compared to standard imaging techniques, and outline promising future directions and opportunities that neuromorphic vision offers.
Abstract (translated)
神经形态（或事件）相机代表了传统视觉传感方法的重大转变，将检测到的即时像素光照变化编码为异步事件数据包流。与标准摄像头相比，其创新之处在于从固定时间间隔捕获完整图像帧转变为一种稀疏的数据格式，这种新型格式凭借其独特性质，在各种应用中具有潜在改进空间。然而，这些优势需要重新发明或适应现有算法以有效处理新的数据格式。在本综述中，我们系统地从三个主要维度研究神经形态视觉技术。 首先，我们概述了神经形态相机自诞生以来的技术演变及其独特的硬件特性，并涵盖了最新模型的发展情况。 其次，我们回顾专门为事件数据开发的图像处理算法，包括关键作品中的特征检测、跟踪和光流（这些构成了分析图像元素及变化的基础），以及深度估计和姿态估计或对象识别（用于解释更复杂的场景结构和组件）。这些技术结合了传统的计算机视觉方法与现代数据驱动的方法，用以展示事件相机在广泛应用领域的广阔前景。 第三，我们介绍了一些实用的应用案例研究，展示了事件摄像机如何成功应用于各种行业和场景中。 最后，我们分析了阻碍广泛采用的挑战，识别出与标准成像技术相比的重要研究缺口，并概述神经形态视觉未来有前景的发展方向及机遇。
URL
https://arxiv.org/abs/2504.08588
