Abstract
Event cameras are emerging vision sensors, whose noise is challenging to characterize. Existing denoising methods for event cameras 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. This work proposes, 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 1-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 showing its efficacy on motion estimation and intensity reconstruction tasks. We believe that the proposed approach contributes to strengthening the theory of event-data denoising, as well as impacting practical denoising use-cases, as we release the code upon acceptance. Project page: this https URL
Abstract (translated)
事件相机是一种新兴的视觉传感器，其噪声特征难以表征。现有的事件相机去噪方法通常将运动估计等其他任务与去噪过程分开处理（即，在去噪后依次进行）。然而，运动是事件数据的一个内在组成部分，因为如果没有物体或摄像机自身的移动，场景边缘无法被感知到。本工作提出了一种据我们所知首次同时估计各种形式的运动（例如自运动、光流）和噪声的方法。该方法具有灵活性，可以在广泛应用的对比最大化框架中替换1步运动估计算法，并可采用包括深度神经网络在内的其他运动估算器。 实验结果表明，在E-MLB去噪基准测试上，所提方法达到了当前最佳性能；在DND21基准测试上也取得了有竞争力的结果。同时，它还在运动估计和强度重建任务中证明了其有效性。我们认为该提出的办法有助于加强事件数据去噪理论，并且通过发布代码，将对实际应用中的去噪案例产生影响。 项目页面：[此链接](https://this-url)（请根据实际情况填写URL）。
URL
https://arxiv.org/abs/2504.04029
