  Optical flow estimation in omnidirectional videos faces two significant
issues: the lack of benchmark datasets and the challenge of adapting
perspective video-based methods to accommodate the omnidirectional nature. This
paper proposes the first perceptually natural-synthetic omnidirectional
benchmark dataset with a 360-degree field of view, FLOW360, with 40 different
videos and 4,000 video frames. We conduct comprehensive characteristic analysis
and comparisons between our dataset and existing optical flow datasets, which
manifest perceptual realism, uniqueness, and diversity. To accommodate the
omnidirectional nature, we present a novel Siamese representation Learning
framework for Omnidirectional Flow (SLOF). We train our network in a
contrastive manner with a hybrid loss function that combines contrastive loss
and optical flow loss. Extensive experiments verify the proposed framework's
effectiveness and show up to 40% performance improvement over the
state-of-the-art approaches. Our FLOW360 dataset and code are available at
https://siamlof.github.io/.
