论文标题
实例分割可见的和遮挡区域，用于从一堆对象找到和挑选目标
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects
论文作者
论文摘要
我们提出了一个机器人系统，用于从一堆物体中挑选目标，该物体能够通过以适当的顺序消除障碍物来查找和抓住目标对象。基本的想法是分段具有可见和遮挡面具的实例，我们称之为“实例遮挡细分”。为了实现这一目标，我们将现有的实例分割模型扩展到具有新颖的“重新构造”体系结构，其中该模型明确地了解了实体关系。同样，通过使用图像合成，我们可以使系统能够处理没有人类注释的新对象。实验结果表明，与传统模型相比，与传统的模型相比，与人类注销的数据集相比，恢复结构的有效性。我们还展示了系统在用真正的机器人中挑选目标的能力。
We present a robotic system for picking a target from a pile of objects that is capable of finding and grasping the target object by removing obstacles in the appropriate order. The fundamental idea is to segment instances with both visible and occluded masks, which we call `instance occlusion segmentation'. To achieve this, we extend an existing instance segmentation model with a novel `relook' architecture, in which the model explicitly learns the inter-instance relationship. Also, by using image synthesis, we make the system capable of handling new objects without human annotations. The experimental results show the effectiveness of the relook architecture when compared with a conventional model and of the image synthesis when compared to a human-annotated dataset. We also demonstrate the capability of our system to achieve picking a target in a cluttered environment with a real robot.
