Pandora: 从第一人称视觉获取拟合3D场景图
Pandora: Articulated 3D Scene Graphs from Egocentric Vision
AI总结：
本文提出Pandora方法，利用人类自然探索场景时的单目数据，恢复拟合物体部件模型，并整合到3D场景图中，提升机器人移动操作能力。
摘要：
机器人映射系统通常通过机器人自身传感器和相机构建度量-语义场景表示。然而，这些
英文摘要：
Robotic mapping systems typically approach building metric-semantic scene representations from the robot's own sensors and cameras. However, these "first person" maps inherit the robot's own limitations due to its embodiment or skillset, which may leave many aspects of the environment unexplored. For example, the robot might not be able to open drawers or access wall cabinets. In this sense, the map representation is not as complete, and requires a more capable robot to fill in the gaps. We narrow these blind spots in current methods by leveraging egocentric data captured as a human naturally explores a scene wearing Project Aria glasses, giving a way to directly transfer knowledge about articulation from the human to any deployable robot. We demonstrate that, by using simple heuristics, we can leverage egocentric data to recover models of articulate object parts, with quality comparable to those of state-of-the-art methods based on other input modalities. We also show how to integrate these models into 3D scene graph representations, leading to a better understanding of object dynamics and object-container relationships. We finally demonstrate that these articulated 3D scene graphs enhance a robot's ability to perform mobile manipulation tasks, showcasing an application where a Boston Dynamics Spot is tasked with retrieving concealed target items, given only the 3D scene graph as input.
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。
