  In this paper, we address the challenging source-free unsupervised domain
adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a
pinhole image pre-trained model (i.e., source) and unlabeled panoramic images
(i.e., target). Tackling this problem is non-trivial due to three critical
challenges: 1) semantic mismatches from the distinct Field-of-View (FoV)
between domains, 2) style discrepancies inherent in the UDA problem, and 3)
inevitable distortion of the panoramic images. To tackle these problems, we
propose 360SFUDA++ that effectively extracts knowledge from the source pinhole
model with only unlabeled panoramic images and transfers the reliable knowledge
to the target panoramic domain. Specifically, we first utilize Tangent
Projection (TP) as it has less distortion and meanwhile slits the
equirectangular projection (ERP) to patches with fixed FoV projection (FFP) to
mimic the pinhole images. Both projections are shown effective in extracting
knowledge from the source model. However, as the distinct projections make it
less possible to directly transfer knowledge between domains, we then propose
Reliable Panoramic Prototype Adaptation Module (RP2AM) to transfer knowledge at
both prediction and prototype levels. RP$^2$AM selects the confident knowledge
and integrates panoramic prototypes for reliable knowledge adaptation.
Moreover, we introduce Cross-projection Dual Attention Module (CDAM), which
better aligns the spatial and channel characteristics across projections at the
feature level between domains. Both knowledge extraction and transfer processes
are synchronously updated to reach the best performance. Extensive experiments
on the synthetic and real-world benchmarks, including outdoor and indoor
scenarios, demonstrate that our 360SFUDA++ achieves significantly better
performance than prior SFUDA methods.
