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.

翻译：对未知物体的精确感知对于自主机器人至关重要，特别是在非结构化环境中操作新颖物体时。然而，现有未知物体实例分割方法常存在过度分割和欠分割问题，导致实例边界不精确，并影响后续的机器人任务（如抓取和放置）成功率。为解决这一挑战，本文提出INSTA-BEER，一种快速且准确的模型无关精细化方法，旨在提升未知物体实例分割性能。该方法采用基于误差引导的精细化策略：首先预测初始分割中的逐像素误差，随后基于这些误差估计值进行分割精细化。我们引入四度量边界误差，该指标在物体实例边界处量化了逐像素的真阳性、真阴性、假阳性和假阴性，从而有效捕捉细粒度与实例级的分割误差。此外，误差引导融合模块将误差信息显式集成至精细化过程中，进一步提升了分割质量。在三个广泛使用的基准数据集上进行的全面评估表明，INSTA-BEER在准确性和推理时间上均优于现有先进模型。同时，一项真实机器人实验验证了该方法在杂乱环境中改善目标物体抓取任务性能的实际应用价值。