用单张图片估物体重构，测试其能否直接支持机器人抓取。
Is Image-based Object Pose Estimation Ready to Support Grasping?
- 基于单张RGB图像评估6自由度物体姿态估计器性能。
- 在物理仿真中使用五种开源模型测试抓取成功率，平均低于40%。
- 揭示当前方法难以独立支撑真实抓取任务，适合研究感知-操作融合者。
我们提出一个框架，用于评估仅需单张RGB图像输入的6-DoF实例级物体姿态估计器。除了了解这些估计器的精度，我们更关注它们能否作为机器人抓取的唯一感知机制。为此，我们在物理模拟器中进行抓取实验，利用图像估计的姿态指导平行夹爪和欠驱动机械手抓取3D物体模型。实验基于BOP数据集的一个子集，对比了五种开源姿态估计器，提供了文献中缺失的关键洞察。
原文摘要 · Abstract (English)
We present a framework for evaluating 6-DoF instance-level object pose estimators, focusing on those that require a single RGB (not RGB-D) image as input. Besides gaining intuition about how accurate these estimators are, we are interested in the degree to which they can serve as the sole perception mechanism for robotic grasping. To assess this, we perform grasping trials in a physics-based simulator, using image-based pose estimates to guide a parallel gripper and an underactuated robotic hand in picking up 3D models of objects. Our experiments on a subset of the BOP (Benchmark for 6D Object Pose Estimation) dataset compare five open-source object pose estimators and provide insights that were missing from the literature.
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