发表机构
Hunan University（湖南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对跨具身全景感知中的观察偏移问题，提出跨具身开放全景分割任务、EmbPASS基准及EPONet网络，通过RAMA和CAST模块提升空间建模与语义迁移，在EmbPASS上以35.82% mIoU超越基线。
AI 中文摘要
全景图像提供完整的360度视野，使得具身感知能够实现全面的场景理解。然而，异构的具身平台在观察视角和空间布局上存在显著差异，导致跨具身观察偏移，这给一致且可靠的全景感知带来了额外挑战，而对此问题的系统性研究仍然有限。为填补这一空白，我们提出了一项新任务，称为跨具身开放全景分割。同时，我们建立了EmbPASS，一个多平台全景语义分割基准，涵盖车辆、无人机、可穿戴设备和四足机器人平台，采用统一语义分类体系，为系统研究跨具身全景感知提供了测试平台。我们进一步提出了EPONet，一种开放词汇的全景语义分割网络，集成了关系感知度量适配器（RAMA）和内容自适应语义迁移（CAST），以增强异构具身观察下的空间建模和语义迁移。大量实验表明，EPONet在EmbPASS上取得了最佳的平台均衡性能，mIoU达到35.82%，比最强基线高出1.10%，同时在现有全景分割基准上保持竞争力。源代码和EmbPASS基准将在此https URL公开提供。
英文摘要
Panoramic images provide a complete 360-degree field of view, enabling comprehensive scene understanding for embodied perception. However, heterogeneous embodied platforms exhibit substantial differences in observation viewpoints and spatial layouts, giving rise to cross-embodiment observation shifts that pose additional challenges to consistent and reliable panoramic perception, while systematic studies of this problem remain limited. To bridge this gap, we introduce a new task, termed Cross-Embodiment Open Panoramic Segmentation. Meanwhile, we establish EmbPASS, a multi-platform panoramic semantic segmentation benchmark spanning Vehicle, Drone, Wearable, and Quadruped platforms under a unified semantic taxonomy, providing a testbed for systematically studying cross-embodiment panoramic perception. We further propose EPONet, an open-vocabulary panoramic semantic segmentation network that integrates Relation-Aware Metric Adapter (RAMA) and Content-Adaptive Semantic Transfer (CAST) to enhance spatial modeling and semantic transfer under heterogeneous embodied observations. Extensive experiments show that EPONet achieves the best platform-balanced performance on EmbPASS with 35.82% mIoU, outperforming the strongest baseline by 1.10%, while remaining competitive on existing panoramic segmentation benchmarks. The source code and EmbPASS benchmark will be made publicly available at https://github.com/guopj1/EmbPASS.
Comments9 pages, 5 figures
