机器人学中单目 6D 物体位姿估计的挑战
Challenges for Monocular 6D Object Pose Estimation in Robotics
AI总结：
本文指出以往综述因范围过广而难以识别单目位姿估计在机器人学中的特有挑战，通过统一视角发现遮挡处理、新位姿表示及类别级位姿估计等仍是基础难题，并强调大型物体集、新物体、折射材料和不确定性估计等核心未解挑战及未来改进方向。
摘要：
物体位姿估计是一项核心感知任务，例如能够实现物体抓取和场景理解。广泛可用、廉价且高分辨率的 RGB 传感器以及允许基于该模态进行快速推理的 CNN，使得单目方法特别适合机器人学应用。我们观察到，以往关于物体位姿估计的综述确立了针对不同模态、单视图和多视图设置，以及考虑众多应用的数据集和指标的最先进水平。然而，我们认为这些工作广泛的范围阻碍了识别单目方法特有的开放性挑战，以及推导其在机器人学应用中有前景的未来挑战。通过提供对来自机器人学和计算机视觉领域近期出版物的统一视角，我们发现遮挡处理、新位姿表示以及形式化和改进类别级位姿估计仍然是与机器人学高度相关的基础挑战。此外，为了进一步提高机器人性能，大型物体集、新物体、折射材料和不确定性估计是核心且很大程度上未解决的开放挑战。为了解决这些问题，本体推理、可变形性处理、场景级推理、真实数据集和算法的生态足迹需要得到改进。
英文摘要：
Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surveys on object pose estimation establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, novel pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
