  In this paper, we address panoramic semantic segmentation which is
under-explored due to two critical challenges: (1) image distortions and object
deformations on panoramas; (2) lack of semantic annotations in the 360{\deg}
imagery. To tackle these problems, first, we propose the upgraded Transformer
for Panoramic Semantic Segmentation, i.e., Trans4PASS+, equipped with
Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for
handling object deformations and image distortions whenever (before or after
adaptation) and wherever (shallow or deep levels). Second, we enhance the
Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification
for unsupervised domain adaptive panoramic segmentation. Third, aside from
Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS)
with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real)
adaptation scheme in 360{\deg} imagery. Extensive experiments are conducted,
which cover indoor and outdoor scenarios, and each of them is investigated with
Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art
performances on four domain adaptive panoramic semantic segmentation
benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.
