EgoXtreme：极端条件下第一人称视角鲁棒物体姿态估计数据集
EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions
该数据集为第一人称视角下的 6D 物体姿态估计提供了极端条件（严重运动模糊、动态光照、烟雾遮挡）下的评测基准，可用于检验现有泛化姿态估计器的失效模式，并推动时序信息利用与鲁棒性方法的研究。
原文依据 Evaluations of state-of-the-art generalizable pose estimators on EgoXtreme indicate that their generalization fails to hold in extreme conditions, especially under low light.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论智能眼镜正在成为一种有用的设备，因为它在双手忙碌、眼睛专注于任务的情况下提供了大量洞察。为了理解佩戴者的上下文，第一人称视角下的 6D 物体姿态估计正变得至关重要。然而，现有的 6D 物体姿态估计基准未能捕捉真实世界第一人称应用中的挑战，这些应用通常以严重的运动模糊、动态光照和视觉遮挡为主。这种差异在受控的实验室数据与混乱的真实世界应用之间造成了显著差距。为了弥合这一差距，我们引入了 EgoXtreme，一个完全从第一人称视角捕获的新的大规模 6D 姿态估计数据集。EgoXtreme 具有三个具有挑战性的场景——工业维护、运动和紧急救援——旨在通过极端光照、严重运动模糊和烟雾引入严重的感知歧义。在 EgoXtreme 上对最优的泛化姿态估计器进行的评估表明，它们的泛化在极端条件下无法保持，尤其是在低光照下。我们进一步证明，简单地应用图像复原（例如去模糊）对极端条件没有提供正向改进。虽然基于跟踪的方法出现了性能提升，这意味着在快速运动场景中使用时序信息是有意义的。我们得出结论，EgoXtreme 是开发和评估下一代足够鲁棒、适用于真实世界第一人称视觉的姿态估计模型的重要资源。数据集和代码可在 https://taegyoun88.github.io/EgoXtreme/ 获取。
英文原文摘要 1614 字符
Smart glass is emerging as an useful device since it provides plenty of insights under hands-busy, eyes-on-task situations. To understand the context of the wearer, 6D object pose estimation in egocentric view is becoming essential. However, existing 6D object pose estimation benchmarks fail to capture the challenges of real-world egocentric applications, which are often dominated by severe motion blur, dynamic illumination, and visual obstructions. This discrepancy creates a significant gap between controlled lab data and chaotic real-world application. To bridge this gap, we introduce EgoXtreme, a new large-scale 6D pose estimation dataset captured entirely from an egocentric perspective. EgoXtreme features three challenging scenarios--industrial maintenance, sports, and emergency rescue--designed to introduce severe perceptual ambiguities through extreme lighting, heavy motion blur, and smoke. Evaluations of state-of-the-art generalizable pose estimators on EgoXtreme indicate that their generalization fails to hold in extreme conditions, especially under low light. We further demonstrate that simply applying image restoration (e.g., deblurring) offers no positive improvement for extreme conditions. While performance gain has appeared in tracking-based approach, implying using temporal information in fast-motion scenarios is meaningful. We conclude that EgoXtreme is an essential resource for developing and evaluating the next generation of pose estimation models robust enough for real-world egocentric vision. The dataset and code are available at https://taegyoun88.github.io/EgoXtreme/