Adapting Segment Anything Model for Unseen Object Instance Segmentation

• URL: http://arxiv.org/abs/2409.15481v1
• Date: Mon, 23 Sep 2024 19:05:50 GMT
• Title: Adapting Segment Anything Model for Unseen Object Instance Segmentation
• Authors: Rui Cao, Chuanxin Song, Biqi Yang, Jiangliu Wang, Pheng-Ann Heng, Yun-Hui Liu,
• Abstract summary: Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. 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.
• Score: 70.60171342436092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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