首次同步估计事件相机运动与噪声，提升去噪与重建效果
Simultaneous Motion And Noise Estimation with Event Cameras
- 同时建模运动与噪声，打破传统分步处理流程
- 在E-MLB上达当前最优，DND21上表现竞争力
- 支持替换任意运动估计算法，适配深度网络等新方法
事件相机是新兴视觉传感器，其噪声难以准确建模。现有去噪方法通常独立设计，将运动估计等任务放在去噪之后（即串行处理）。然而，运动是事件数据的固有特性，因为场景边缘必须依赖运动才能被感知。本文提出，据我们所知，首个能同时估计多种运动形式（如自身运动、光流）和噪声的方法。该方法灵活，可将广泛使用的对比度最大化框架中的一步运动估计替换为任意其他运动估计算法，如深度神经网络。实验表明，该方法在E-MLB去噪基准上达到领先水平，在DND21基准上表现优异，并在运动估计与强度重建任务中均展现有效性。本方法推动事件数据去噪理论发展，并通过开源代码拓展实际应用。项目页面：https://github.com/tub-rip/ESMD
原文摘要 · Abstract (English)
Event cameras are emerging vision sensors whose noise is challenging to characterize. Existing denoising methods for event cameras are often designed in isolation and thus consider other tasks, such as motion estimation, separately (i.e., sequentially after denoising). However, motion is an intrinsic part of event data, since scene edges cannot be sensed without motion. We propose, to the best of our knowledge, the first method that simultaneously estimates motion in its various forms (e.g., ego-motion, optical flow) and noise. The method is flexible, as it allows replacing the one-step motion estimation of the widely-used Contrast Maximization framework with any other motion estimator, such as deep neural networks. The experiments show that the proposed method achieves state-of-the-art results on the E-MLB denoising benchmark and competitive results on the DND21 benchmark, while demonstrating effectiveness across motion estimation and intensity reconstruction tasks. Our approach advances event-data denoising theory and expands practical denoising use-cases via open-source code. Project page: https://github.com/tub-rip/ESMD
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