6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and occlusion between objects of the same type may cause confusion even in well-trained models. We propose a novel method of hard example synthesis that is model-agnostic, using existing simulators and the modeling of pose error in both the camera-to-object viewsphere and occlusion space. Through evaluation of the model performance with respect to the distribution of object poses and occlusions, we discover regions of high error and generate realistic training samples to specifically target these regions. With our training approach, we demonstrate an improvement in correct detection rate of up to 20% across several ROBI-dataset objects using state-of-the-art pose estimation models.

翻译：六维物体姿态估计是机器人学中实现高效环境交互的基础组件。在料箱拾取应用中，该任务尤其具有挑战性：物体可能缺乏纹理且处于复杂姿态，同类物体间的遮挡甚至会使训练有素的模型产生混淆。我们提出一种与模型无关的困难样本合成新方法，该方法利用现有仿真器，并在相机-物体视球空间与遮挡空间中对姿态误差进行建模。通过评估模型在物体姿态与遮挡分布上的性能表现，我们识别出高误差区域，并生成逼真的训练样本来针对性覆盖这些区域。采用我们的训练方法，我们在多个ROBI数据集物体上使用最先进的姿态估计模型，实现了正确检测率最高达20%的提升。