Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimation-then-refinement scheme, the model first estimates the pixel-wise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications.

翻译：高效且精准地分割未知物体对于机器人操控至关重要。然而，由于过分割或欠分割问题，这一任务仍具挑战性。尽管现有精细化方法能提升分割质量，但它们仅能修正微小的边界误差，或速度不够快。本文提出一种新颖的精细化模型——实例边界显式误差估计与精细化（INSTA-BEEER），该模型支持实例的增删与边界锐化。借助“先估计误差、再精细化”的方案，模型首先逐像素估计初始分割中实例边界的显式误差：真阳性、真阴性、假阳性及假阴性像素。随后，模型以这些误差估计为指导，对初始分割进行精细化。实验表明，所提模型显著提升分割效果，达到当前最优性能。此外，该模型运行速度快（小于0.1秒），能持续改善多种初始分割方法的性能，因而高度适用于实际机器人应用场景。