A comprehensive review of event camera-based depth estimation methods

Depth estimation serves as the foundation for many computer vision applications. As an emerging perception sensor inspired by biological retinas, event cameras have garnered significant attention in recent years due to their high dynamic range, ultra-high temporal resolution, low latency, and other distinctive features.This paper provides a systematic review of depth estimation methods based on event cameras, including related datasets, evaluation metrics, and future development prospects. In terms of methodological classification, the approaches are divided into frame-based methods and learning-based methods (the latter further subdivided into Spiking Neural Networks (SNN) and Artificial Neural Network (ANN)) based on sensor type. Additionally, the supervision modes (supervised/unsupervised) and open-source availability of these methods are examined in detail, summarizing the algorithmic principles, performance, and contributions of various approaches. Subsequently, commonly used datasets and measurement techniques in event camera-based depth estimation are systematically organized. Finally, the development prospects of this field are discussed. We hope this paper can offer a comprehensive survey and serve as an introductory reference for researchers new to this field.