Optical flow estimation is a crucial task in computer vision that provides low-level motion
information. Despite recent advances, real-world applications still present significant …

Motion analysis is one of the most fundamental and challenging problems in the field of
computer vision, which can be widely applied in many areas, such as autonomous driving …

Akin to many subareas of computer vision, the recent advances in deep learning have also
significantly influenced the literature on optical flow. Previously, the literature had been …

To date, top-performing optical flow estimation methods only take pairs of consecutive
frames into account. While elegant and appealing, the idea of using more than two frames …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …

Optical flow estimation plays an important role in many multimedia and computer vision
tasks. Although great progress has been made in applying convolutional neural networks …

The accuracy of optical flow estimation algorithms has been improving steadily as
evidenced by results on the Middlebury optical flow benchmark. The typical formulation …

Many classical and learning-based optical flow methods rely on hierarchical concepts to
improve both accuracy and robustness. However, one of the currently most successful …

Event cameras are bio-inspired sensors that asynchronously report intensity changes in
microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously …