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arXiv · 2409.15481

Adapting Segment Anything Model for Unseen Object Instance Segmentation

Rui Cao·Chuanxin Song·Biqi Yang·Jiangliu Wang·Pheng-Ann Heng·Yun-Hui Liu
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Abstract

Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments. Previous approaches require full supervision on large-scale tabletop datasets for effective pretraining. In this paper, we propose UOIS-SAM, a data-efficient solution for the UOIS task that leverages SAM's high accuracy and strong generalization capabilities. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder, mitigating issues introduced by the SAM baseline, such as background confusion and over-segmentation, especially in scenarios involving occlusion and texture-rich objects. Extensive experimental results on OCID, OSD, and additional photometrically challenging datasets including PhoCAL and HouseCat6D, demonstrate that, even using only 10% of the training samples compared to previous methods, UOIS-SAM achieves state-of-the-art performance in unseen object segmentation, highlighting its effectiveness and robustness in various tabletop scenes.

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Rui Cao, Chuanxin Song, Biqi Yang, Jiangliu Wang, Pheng-Ann Heng, Yun-Hui Liu. 2024-09-23. Adapting Segment Anything Model for Unseen Object Instance Segmentation. https://arxiv.org/abs/2409.15481

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