RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
This addresses the challenge for robots operating in unstructured environments, representing an incremental improvement over existing segmentation methods.
The paper tackles the problem of segmenting unseen object instances in cluttered environments by proposing a framework that refines instance masks using graph neural networks, achieving state-of-the-art performance when combined with previous methods.
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have exhibited promising results, they still tend to provide incorrect results in highly cluttered scenes. We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial. Thus, in this work, we propose a novel framework that refines the output of such methods by utilizing a graph-based representation of instance masks. We train deep networks capable of sampling smart perturbations to the segmentations, and a graph neural network, which can encode relations between objects, to evaluate the perturbed segmentations. Our proposed method is orthogonal to previous works and achieves state-of-the-art performance when combined with them. We demonstrate an application that uses uncertainty estimates generated by our method to guide a manipulator, leading to efficient understanding of cluttered scenes. Code, models, and video can be found at https://github.com/chrisdxie/rice .