综述事件相机在三维重建中的应用进展，涵盖不同方法与未来方向。
A Survey on Event-driven 3D Reconstruction: Development under Different Categories
- 按几何、学习、混合三类梳理事件驱动三维重建方法
- 覆盖立体、单目及多模态系统，包含神经辐射场等新趋势
- 适合关注事件相机与3D视觉融合的研究者参考
事件相机因其高时间分辨率、低延迟和高动态范围，在三维重建领域受到越来越多关注。它们以异步方式捕捉像素级亮度变化，可在快速运动和复杂光照条件下实现精确重建。本文全面综述了基于事件的三维重建方法，包括立体、单目及多模态系统。进一步根据几何、基于学习和混合方法进行分类，涵盖神经辐射场与3D高斯溅射等新兴趋势。相关工作按时间脉络组织，展示该领域的创新与发展。为支持未来研究，还指出了数据集、实验设计、评估指标、事件表示等方面的关键研究空白与发展方向。
原文摘要 · Abstract (English)
Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, learning-based, and hybrid approaches. Emerging trends, such as neural radiance fields and 3D Gaussian splatting with event data, are also covered. The related works are structured chronologically to illustrate the innovations and progression within the field. To support future research, we also highlight key research gaps and future research directions in dataset, experiment, evaluation, event representation, etc.
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