  Panoramic videos contain richer spatial information and have attracted
tremendous amounts of attention due to their exceptional experience in some
fields such as autonomous driving and virtual reality. However, existing
datasets for video segmentation only focus on conventional planar images. To
address the challenge, in this paper, we present a panoramic video dataset,
PanoVOS. The dataset provides 150 videos with high video resolutions and
diverse motions. To quantify the domain gap between 2D planar videos and
panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS)
models on PanoVOS. Through error analysis, we found that all of them fail to
tackle pixel-level content discontinues of panoramic videos. Thus, we present a
Panoramic Space Consistency Transformer (PSCFormer), which can effectively
utilize the semantic boundary information of the previous frame for pixel-level
matching with the current frame. Extensive experiments demonstrate that
compared with the previous SOTA models, our PSCFormer network exhibits a great
advantage in terms of segmentation results under the panoramic setting. Our
dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can
advance the development of panoramic segmentation/tracking.
