Panoramic Out-of-Distribution Segmentation

• URL: http://arxiv.org/abs/2505.03539v1
• Date: Tue, 06 May 2025 13:51:26 GMT
• Title: Panoramic Out-of-Distribution Segmentation
• Authors: Mengfei Duan, Kailun Yang, Yuheng Zhang, Yihong Cao, Fei Teng, Kai Luo, Jiaming Zhang, Zhiyong Li, Shutao Li,
• Abstract summary: We introduce a new task, Panoramic Out-of-distribution (PanOoS), achieving OoS for panoramas.<n> POS adapts to the characteristics of panoramic images through text-guided prompt distribution learning.<n>Experiments demonstrate superior performance of POS, with AuPRC improving by 34.25% and FPR95 decreasing by 21.42%.
• Score: 28.962062029634584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Panoramic imaging enables capturing 360{\deg} images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception. However, current panoramic semantic segmentation methods fail to identify outliers, and pinhole Out-of-distribution Segmentation (OoS) models perform unsatisfactorily in the panoramic domain due to background clutter and pixel distortions. To address these issues, we introduce a new task, Panoramic Out-of-distribution Segmentation (PanOoS), achieving OoS for panoramas. Furthermore, we propose the first solution, POS, which adapts to the characteristics of panoramic images through text-guided prompt distribution learning. Specifically, POS integrates a disentanglement strategy designed to materialize the cross-domain generalization capability of CLIP. The proposed Prompt-based Restoration Attention (PRA) optimizes semantic decoding by prompt guidance and self-adaptive correction, while Bilevel Prompt Distribution Learning (BPDL) refines the manifold of per-pixel mask embeddings via semantic prototype supervision. Besides, to compensate for the scarcity of PanOoS datasets, we establish two benchmarks: DenseOoS, which features diverse outliers in complex environments, and QuadOoS, captured by a quadruped robot with a panoramic annular lens system. Extensive experiments demonstrate superior performance of POS, with AuPRC improving by 34.25% and FPR95 decreasing by 21.42% on DenseOoS, outperforming state-of-the-art pinhole-OoS methods. Moreover, POS achieves leading closed-set segmentation capabilities. Code and datasets will be available at https://github.com/MengfeiD/PanOoS.

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