This paper addresses an interesting yet challenging problem-- source-free
unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic
segmentation--given only a pinhole image-trained model (i.e., source) and
unlabeled panoramic images (i.e., target). Tackling this problem is nontrivial
due to the semantic mismatches, style discrepancies, and inevitable distortion
of panoramic images. To this end, we propose a novel method that utilizes
Tangent Projection (TP) as it has less distortion and meanwhile slits the
equirectangular projection (ERP) with a fixed FoV to mimic the pinhole images.
Both projections are shown effective in extracting knowledge from the source
model. However, the distinct projection discrepancies between source and target
domains impede the direct knowledge transfer; thus, we propose a panoramic
prototype adaptation module (PPAM) to integrate panoramic prototypes from the
extracted knowledge for adaptation. We then impose the loss constraints on both
predictions and prototypes and propose a cross-dual attention module (CDAM) at
the feature level to better align the spatial and channel characteristics
across the domains and projections. 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 method achieves significantly better
performance than prior SFUDA methods for pinhole-to-panoramic adaptation.

本文提出了一种用于针孔图像到全景图像语义分割的无源无监督域自适应（SFUDA）的新方法，该方法利用了割裂等距投影、固定视场角的损失和全景原型自适应模块（PPAM），以及跨双重注意模块（CDAM），通过提取并转移知识实现域间适应。实验证明，该方法在合成和真实世界基准测试中表现显著优于现有的 SFUDA 方法。