  Panoramic images, capturing a 360{\deg} field of view (FoV), encompass
omnidirectional spatial information crucial for scene understanding. However,
it is not only costly to obtain training-sufficient dense-annotated panoramas
but also application-restricted when training models in a close-vocabulary
setting. To tackle this problem, in this work, we define a new task termed Open
Panoramic Segmentation (OPS), where models are trained with FoV-restricted
pinhole images in the source domain in an open-vocabulary setting while
evaluated with FoV-open panoramic images in the target domain, enabling the
zero-shot open panoramic semantic segmentation ability of models. Moreover, we
propose a model named OOOPS with a Deformable Adapter Network (DAN), which
significantly improves zero-shot panoramic semantic segmentation performance.
To further enhance the distortion-aware modeling ability from the pinhole
source domain, we propose a novel data augmentation method called Random
Equirectangular Projection (RERP) which is specifically designed to address
object deformations in advance. Surpassing other state-of-the-art
open-vocabulary semantic segmentation approaches, a remarkable performance
boost on three panoramic datasets, WildPASS, Stanford2D3D, and Matterport3D,
proves the effectiveness of our proposed OOOPS model with RERP on the OPS task,
especially +2.2% on outdoor WildPASS and +2.4% mIoU on indoor Stanford2D3D. The
source code is publicly available at
https://junweizheng93.github.io/publications/OPS/OPS.html.
