Optical Flow
Overview
Optical flow estimates a motion vector for each image pixel, helping systems track objects, stabilize video, guide robots, and interpolate or compress frames. Methods range from classical local and variational estimation to deep correlation-based networks such as RAFT, global matching transformers, and models that use pretrained vision features; event-camera methods handle asynchronous motion cues. Training commonly uses synthetic labeled data, while self-supervised approaches exploit photometric, geometric, and temporal consistency when labels are scarce. A central challenge is making accurate estimates transfer to real scenes with occlusion, large motion, lighting changes, and image corruption without excessive computation.
Latest papers
58 papers9 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.