In order to function in unstructured environments, robots need the ability to
recognize unseen novel objects. We take a step in this direction by tackling
the problem of segmenting unseen object instances in tabletop environments.
However, the type of large-scale real-world dataset required for this task
typically does not exist for most robotic settings, which motivates the use of
synthetic data. We propose a novel method that separately leverages synthetic
RGB and synthetic depth for unseen object instance segmentation. Our method is
comprised of two stages where the first stage operates only on depth to produce
rough initial masks, and the second stage refines these masks with RGB.
Surprisingly, our framework is able to learn from synthetic RGB-D data where
the RGB is non-photorealistic. To train our method, we introduce a large-scale
synthetic dataset of random objects on tabletops. We show that our method,
trained on this dataset, can produce sharp and accurate masks, outperforming
state-of-the-art methods on unseen object instance segmentation. We also show
that our method can segment unseen objects for robot grasping. Code, models and
video can be found at
this https URL

本篇论文提出了一种使用合成 RGB 和深度图像数据实现表面外目标实例分割的新方法，该方法能够学习非真实感 RGB-D 数据，并在机器人抓取任务中表现出色。