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.

翻译：全景视频包含更丰富的空间信息，因其在自动驾驶、虚拟现实等领域的卓越体验而受到广泛关注。然而，现有视频分割数据集仅聚焦于传统平面图像。为解决这一挑战，本文提出了全景视频数据集PanoVOS。该数据集包含150个高分辨率、运动多样的视频。为量化二维平面视频与全景视频之间的领域差距，我们在PanoVOS上评估了15种现成的视频目标分割（VOS）模型。通过误差分析发现，所有模型均无法处理全景视频中像素级内容的不连续性。为此，我们提出了全景空间一致性Transformer（PSCFormer），该网络能有效利用前一帧的语义边界信息，与当前帧进行像素级匹配。大量实验表明，与先前最先进模型相比，我们的PSCFormer网络在全景设置下的分割结果展现出显著优势。本数据集为全景VOS提出了新挑战，希望PanoVOS能推动全景分割/跟踪领域的发展。