High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement

TL;DR

This paper introduces QuBER, a novel boundary error refinement method for unknown object instance segmentation that significantly improves accuracy and grasping success in robotic manipulation tasks.

Contribution

The paper presents QuBER, a new quadruple boundary error refinement approach that enhances segmentation quality by correcting boundary errors in unknown object segmentation.

Findings

Outperforms state-of-the-art segmentation methods on public benchmarks.

Maintains fast inference time under 0.1 seconds.

Improves grasping success rate in cluttered environments.

Abstract

Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims to identify all objects in unknown categories and backgrounds, has become a key capability for various robotic tasks. However, existing methods struggle with over-segmentation and under-segmentation, leading to failures in manipulation tasks such as grasping. To address these challenges, we propose QuBER (Quadruple Boundary Error Refinement), a novel error-informed refinement approach for high-quality UOIS. QuBER first estimates quadruple boundary errors-true positive, true negative, false positive, and false negative pixels-at the instance boundaries of the initial segmentation. It then refines the segmentation using an error-guided fusion mechanism, effectively correcting both fine-grained and instance-level segmentation errors. Extensive evaluations on three public benchmarks demonstrate that QuBER outperforms state-of-the-art methods and consistently improves various UOIS methods while maintaining a fast inference time of less than 0.1 seconds. Furthermore, we show that QuBER improves the success rate of grasping target objects in cluttered environments. Code and supplementary materials are available at https://sites.google.com/view/uois-quber.