Abstract
Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficient illumination. These conditions lead to weakened texture and amplified noise and deteriorate the appearance saturation and boundary completeness of frame cameras, which are necessary for motion feature matching. In degraded scenes, the frame camera provides dense appearance saturation but sparse boundary completeness due to its long imaging time and low dynamic range. In contrast, the event camera offers sparse appearance saturation, while its short imaging time and high dynamic range gives rise to dense boundary completeness. Traditionally, existing methods utilize feature fusion or domain adaptation to introduce event to improve boundary completeness. However, the appearance features are still deteriorated, which severely affects the mostly adopted discriminative models that learn the mapping from visual features to motion fields and generative models that generate motion fields based on given visual features. So we introduce diffusion models that learn the mapping from noising flow to clear flow, which is not affected by the deteriorated visual features. Therefore, we propose a novel optical flow estimation framework Diff-ABFlow based on diffusion models with frame-event appearance-boundary fusion.
Abstract (translated)
光流估计在传统场景中取得了显著成果，但在高速和低光照条件下则面临挑战。这些条件会导致运动模糊和照明不足，进而削弱纹理、放大噪声，并损害帧相机的外观饱和度和边界完整性，而这些都是进行运动特征匹配所必需的。在这种退化的环境中，由于较长的成像时间和较低的动态范围，帧相机虽然提供密集的外观饱和度，但其边界完整性稀疏。相比之下，事件相机提供了稀疏的外观饱和度，但由于其较短的成像时间和较高的动态范围，它可以产生密集的边界完整性。 传统方法通常采用特征融合或领域适应技术，将事件引入以提高边界的完整性。然而，这些方法并未改善外观特征的质量，这严重阻碍了大多数使用的判别模型（基于视觉特征学习运动场映射）和生成模型（根据给定的视觉特征生成运动场）。因此，我们提出了一个全新的框架——Diff-ABFlow，该框架基于扩散模型，并结合帧事件在外观边界上的融合技术。这种新方法不依赖于退化的视觉特征，而是从模糊光流中学习清晰光流的映射，从而提升在困难条件下的性能。
URL
https://arxiv.org/abs/2510.10577
