  Panoramic images can broaden the Field of View (FoV), occlusion-aware
prediction can deepen the understanding of the scene, and domain adaptation can
transfer across viewing domains. In this work, we introduce a novel task,
Occlusion-Aware Seamless Segmentation (OASS), which simultaneously tackles all
these three challenges. For benchmarking OASS, we establish a new
human-annotated dataset for Blending Panoramic Amodal Seamless Segmentation,
i.e., BlendPASS. Besides, we propose the first solution UnmaskFormer, aiming at
unmasking the narrow FoV, occlusions, and domain gaps all at once.
Specifically, UnmaskFormer includes the crucial designs of Unmasking Attention
(UA) and Amodal-oriented Mix (AoMix). Our method achieves state-of-the-art
performance on the BlendPASS dataset, reaching a remarkable mAPQ of 26.58% and
mIoU of 43.66%. On public panoramic semantic segmentation datasets, i.e.,
SynPASS and DensePASS, our method outperforms previous methods and obtains
45.34% and 48.08% in mIoU, respectively. The fresh BlendPASS dataset and our
source code are available at https://github.com/yihong-97/OASS.
