标题： 解锁约束：无源遮挡感知无缝分割 显示英文标题

标题： Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation

摘要： 全景图像处理对于全场景感知至关重要，但面临如失真、透视遮挡和有限标注等约束。 以往的无监督领域自适应方法将标记的针孔数据的知识转移到未标记的全景图像，但它们需要访问源针孔数据。 为了解决这些问题，我们引入了一个更实际的任务，即无源遮挡感知无缝分割（SFOASS），并提出了其第一个解决方案，称为UNconstrained Learning Omni-Context知识（UNLOCK）。 具体而言，UNLOCK包括两个关键模块：全场景伪标签学习和非模式驱动的上下文学习。 在不依赖源数据或目标标签的情况下，该框架增强了模型，以实现360{\deg }视角覆盖和遮挡感知推理的分割。 此外，我们通过真实到真实和合成到真实适应设置对提出的SFOASS任务进行了基准测试。 实验结果表明，我们的无源方法实现了与有源方法相当的性能，在mAAP上达到10.9，在mAP上达到11.6，并且在mAPQ上比仅源方法提高了+4.3的绝对值。 所有数据和代码将在https://github.com/yihong-97/UNLOCK公开。 显示英文摘要

摘要： Panoramic image processing is essential for omni-context perception, yet faces constraints like distortions, perspective occlusions, and limited annotations. Previous unsupervised domain adaptation methods transfer knowledge from labeled pinhole data to unlabeled panoramic images, but they require access to source pinhole data. To address these, we introduce a more practical task, i.e., Source-Free Occlusion-Aware Seamless Segmentation (SFOASS), and propose its first solution, called UNconstrained Learning Omni-Context Knowledge (UNLOCK). Specifically, UNLOCK includes two key modules: Omni Pseudo-Labeling Learning and Amodal-Driven Context Learning. While adapting without relying on source data or target labels, this framework enhances models to achieve segmentation with 360{\deg} viewpoint coverage and occlusion-aware reasoning. Furthermore, we benchmark the proposed SFOASS task through both real-to-real and synthetic-to-real adaptation settings. Experimental results show that our source-free method achieves performance comparable to source-dependent methods, yielding state-of-the-art scores of 10.9 in mAAP and 11.6 in mAP, along with an absolute improvement of +4.3 in mAPQ over the source-only method. All data and code will be made publicly available at https://github.com/yihong-97/UNLOCK.