The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to
previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently …

Recent deep learning-based optical flow estimators have exhibited impressive performance
in generating local flows between consecutive frames. However, the estimation of long …

Estimating 3D scene flow from a sequence of monocular images has been gaining
increased attention due to the simple, economical capture setup. Owing to the severe ill …

Motion fields estimated from image data have been widely used in physics and engineering.
Time-resolved particle image velocimetry (TR-PIV) is considered as an advanced flow …

We present a method for estimating dense continuous-time optical flow from event data.
Traditional dense optical flow methods compute the pixel displacement between two …

Occlusions have long been a hard nut to crack in optical flow estimation due to ambiguous
pixels matching between abutting images. Current methods only take two consecutive …

In the domain of video tracking existing methods often grapple with a trade-off between
spatial density and temporal range. Current approaches in dense optical flow estimators …

Studies have shown that the observed image texture details and semantic information are of
great significance for the depth estimation on the road scenes. However, there are …

Tracking dense 3D motion from monocular videos remains challenging, particularly when
aiming for pixel-level precision over long sequences. We introduce\Approach, a novel …