  The ability of scene understanding has sparked active research for panoramic
image semantic segmentation. However, the performance is hampered by distortion
of the equirectangular projection (ERP) and a lack of pixel-wise annotations.
For this reason, some works treat the ERP and pinhole images equally and
transfer knowledge from the pinhole to ERP images via unsupervised domain
adaptation (UDA). However, they fail to handle the domain gaps caused by: 1)
the inherent differences between camera sensors and captured scenes; 2) the
distinct image formats (e.g., ERP and pinhole images). In this paper, we
propose a novel yet flexible dual-path UDA framework, DPPASS, taking ERP and
tangent projection (TP) images as inputs. To reduce the domain gaps, we propose
cross-projection and intra-projection training. The cross-projection training
includes tangent-wise feature contrastive training and prediction consistency
training. That is, the former formulates the features with the same projection
locations as positive examples and vice versa, for the models' awareness of
distortion, while the latter ensures the consistency of cross-model predictions
between the ERP and TP. Moreover, adversarial intra-projection training is
proposed to reduce the inherent gap, between the features of the pinhole images
and those of the ERP and TP images, respectively. Importantly, the TP path can
be freely removed after training, leading to no additional inference cost.
Extensive experiments on two benchmarks show that our DPPASS achieves +1.06$\%$
mIoU increment than the state-of-the-art approaches.
