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In this paper, we address panoramic semantic segmentation which isunder-explored due to two critical challenges: (1) image distortions and objectdeformations on panoramas; (2) lack of semantic annotations in the 360-degreeimagery. To tackle these problems, first, we propose the upgraded Transformerfor Panoramic Semantic Segmentation, i.e., Trans4PASS+, equipped withDeformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules forhandling object deformations and image distortions whenever (before or afteradaptation) and wherever (shallow or deep levels). Second, we enhance theMutual Prototypical Adaptation (MPA) strategy via pseudo-label rectificationfor unsupervised domain adaptive panoramic segmentation. Third, aside fromPinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS)with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real)adaptation scheme in 360-degree imagery. Extensive experiments are conducted,which cover indoor and outdoor scenarios, and each of them is investigated withPin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-artperformances on four domain adaptive panoramic semantic segmentationbenchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.

Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation