From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911682906816512 |
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| author | Zhang, Qifan Allu, Sai Haneesh Wang, Jikai Lu, Yangxiao Xiang, Yu |
| author_facet | Zhang, Qifan Allu, Sai Haneesh Wang, Jikai Lu, Yangxiao Xiang, Yu |
| contents | Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of template images, a robot must locate and segment a specific object instance in a cluttered, previously unseen scene. Existing proposal-based approaches are highly sensitive to proposal quality and often fail under occlusion and background clutter. We propose L2G-Det, a local-to-global instance detection framework that bypasses explicit object proposals by leveraging dense patch-level matching between templates and the query image. Locally matched patches generate candidate points, which are refined through a candidate selection module to suppress false positives. The filtered points are then used to prompt an augmented Segment Anything Model (SAM) with instance-specific object tokens, enabling reliable reconstruction of complete instance masks. Experiments demonstrate improved performance over proposal-based methods in challenging open-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03577 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes Zhang, Qifan Allu, Sai Haneesh Wang, Jikai Lu, Yangxiao Xiang, Yu Computer Vision and Pattern Recognition Robotics Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of template images, a robot must locate and segment a specific object instance in a cluttered, previously unseen scene. Existing proposal-based approaches are highly sensitive to proposal quality and often fail under occlusion and background clutter. We propose L2G-Det, a local-to-global instance detection framework that bypasses explicit object proposals by leveraging dense patch-level matching between templates and the query image. Locally matched patches generate candidate points, which are refined through a candidate selection module to suppress false positives. The filtered points are then used to prompt an augmented Segment Anything Model (SAM) with instance-specific object tokens, enabling reliable reconstruction of complete instance masks. Experiments demonstrate improved performance over proposal-based methods in challenging open-world settings. |
| title | From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2603.03577 |