Unseen Object Instance Segmentation for Robotic Environments
It addresses the challenge of object recognition in unstructured robotic settings where real-world datasets are scarce, though it is incremental as it builds on existing segmentation approaches.
The paper tackles the problem of segmenting unseen object instances in tabletop environments for robots, using a method called UOIS-Net that leverages synthetic RGB and depth data, and it outperforms state-of-the-art methods by producing sharp and accurate masks.
In order to function in unstructured environments, robots need the ability to recognize unseen 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. Our proposed method, UOIS-Net, separately leverages synthetic RGB and synthetic depth for unseen object instance segmentation. UOIS-Net is comprised of two stages: first, it operates only on depth to produce object instance center votes in 2D or 3D and assembles them into rough initial masks. Secondly, these initial masks are refined using 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 can produce sharp and accurate segmentation 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.