Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
This addresses a practical constraint in panoramic image processing for applications requiring 360° viewpoint coverage and occlusion-aware reasoning, though it appears incremental as it builds on existing unsupervised domain adaptation methods.
The paper tackles the problem of panoramic image segmentation without access to source pinhole data, introducing a source-free method that achieves state-of-the-art scores of 10.9 in mAAP and 11.6 in mAP, with a +4.3 mAPQ improvement over source-only methods.
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° 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.