  Accurate perception of unknown objects is essential for autonomous robots,
particularly when manipulating novel items in unstructured environments.
However, existing unknown object instance segmentation (UOIS) methods often
have over-segmentation and under-segmentation problems, resulting in inaccurate
instance boundaries and failures in subsequent robotic tasks such as grasping
and placement. To address this challenge, this article introduces INSTA-BEER, a
fast and accurate model-agnostic refinement method that enhances the UOIS
performance. The model adopts an error-informed refinement approach, which
first predicts pixel-wise errors in the initial segmentation and then refines
the segmentation guided by these error estimates. We introduce the quad-metric
boundary error, which quantifies pixel-wise true positives, true negatives,
false positives, and false negatives at the boundaries of object instances,
effectively capturing both fine-grained and instance-level segmentation errors.
Additionally, the Error Guidance Fusion (EGF) module explicitly integrates
error information into the refinement process, further improving segmentation
quality. In comprehensive evaluations conducted on three widely used benchmark
datasets, INSTA-BEER outperformed state-of-the-art models in both accuracy and
inference time. Moreover, a real-world robotic experiment demonstrated the
practical applicability of our method in improving the performance of target
object grasping tasks in cluttered environments.
