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.

本文研究了针对针孔到全景语义分割的无源无监督域自适应（SFUDA）问题，通过仅有预训练的针孔图像模型（即源模型）和未标记的全景图像（即目标模型）。我们提出了一种名为 360SFUDA ++ 的方法，有效地从源针孔模型中提取知识，并将可靠的知识转移到目标全景领域。我们利用切线投影（TP）和等面积投影（ERP）分别进行知识提取，并引入了 RP2AM 和 CDAM 模块以更好地转移和对齐知识。通过在合成和真实场景的广泛实验中表明，我们的方法相较于先前的 SFUDA 方法具有显著更好的表现。