Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View (FoV) and fragmented observations, often missing critical holistic environmental context. In this paper, we present the first exploration into Panoramic Affordance Prediction, utilizing 360-degree imagery to capture global spatial relationships and holistic scene understanding. To facilitate this novel task, we first introduce PAP-12K, a large-scale benchmark dataset containing over 1,000 ultra-high-resolution (12k, 11904 x 5952) panoramic images with over 12k carefully annotated QA pairs and affordance masks. Furthermore, we propose PAP, a training-free, coarse-to-fine pipeline inspired by the human foveal visual system to tackle the ultra-high resolution and severe distortion inherent in panoramic images. PAP employs recursive visual routing via grid prompting to progressively locate targets, applies an adaptive gaze mechanism to rectify local geometric distortions, and utilizes a cascaded grounding pipeline to extract precise instance-level masks. Experimental results on PAP-12K reveal that existing affordance prediction methods designed for standard perspective images suffer severe performance degradation and fail due to the unique challenges of panoramic vision. In contrast, PAP framework effectively overcomes these obstacles, significantly outperforming state-of-the-art baselines and highlighting the immense potential of panoramic perception for robust embodied intelligence.

翻译：可供性预测在具身人工智能中扮演着感知与行动之间的关键桥梁。然而，现有研究局限于针孔相机模型，其视野狭窄且观测结果碎片化，常常遗漏关键的整体环境上下文。本文首次探索全景可供性预测，利用360度图像捕捉全局空间关系与整体场景理解。为推进这一新颖任务，我们首先引入了PAP-12K，一个大规模基准数据集，包含超过1,000张超高分辨率（12k，11904 x 5952）全景图像，并配有超过12,000个精心标注的问答对及可供性掩码。此外，我们提出了PAP，一个受人类中央凹视觉系统启发的、无需训练、由粗到精的处理流程，以应对全景图像固有的超高分辨率与严重畸变问题。PAP通过网格提示递归地进行视觉路由以逐步定位目标，应用自适应注视机制校正局部几何畸变，并利用级联式定位流程提取精确的实例级掩码。在PAP-12K上的实验结果表明，为标准透视图像设计的现有可供性预测方法因全景视觉的独特挑战而性能严重下降甚至失效。相比之下，PAP框架有效克服了这些障碍，显著超越了现有最先进的基线方法，凸显了全景感知对于鲁棒具身智能的巨大潜力。