Abstract
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 this https URL
Abstract (translated)
智能眼镜正逐渐成为一种实用设备，因其在双手忙碌、眼睛需专注任务的场景中能提供大量洞察。为理解佩戴者的环境上下文，基于第一人称视角的6D物体姿态估计变得至关重要。然而，现有6D物体姿态估计基准数据集未能捕捉真实世界第一人称应用中的挑战，这些场景通常受严重运动模糊、动态光照和视觉遮挡的影响。这种差异造成了受控实验室数据与混乱现实应用之间的显著差距。为弥合这一差距，我们推出了EgoXtreme——一个全新的大规模6D姿态估计数据集，完全从第一人称视角采集。EgoXtreme涵盖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强烈的感知歧义。在EgoXtreme上对最先进的可泛化姿态估计器进行评估表明，它们的泛化能力在极端条件下（尤其是低光照环境）无法保持。我们进一步证明，单纯应用图像修复（如去模糊）对极端条件并无积极改进。而基于跟踪的方法出现了性能提升，暗示在高速运动场景中利用时序信息具有意义。我们认为，EgoXtreme是开发和评估下一代足够稳健以应对真实世界第一人称视觉的姿态估计模型的重要资源。数据集与代码可通过此链接获取。
URL
https://arxiv.org/abs/2603.25135
