Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917547662639104 |
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| author | Großkopf, Erik Kundu, Soumya Snigdha Möller, Hendrik Münster, Nicolas Astaraki, Mehdi Buzduga, Paula Tamara Ritter, Kerstin Wiestler, Benedikt Kirschke, Jan Shapey, Jonathan Vercauteren, Tom Kofler, Florian |
| author_facet | Großkopf, Erik Kundu, Soumya Snigdha Möller, Hendrik Münster, Nicolas Astaraki, Mehdi Buzduga, Paula Tamara Ritter, Kerstin Wiestler, Benedikt Kirschke, Jan Shapey, Jonathan Vercauteren, Tom Kofler, Florian |
| contents | The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between predicted and ground truth segments, which is only straightforward when the IoU threshold exceeds 0.5. Below 0.5, multiple matching strategies emerge in a poorly explored problem space. We systematically elucidate this space by recasting segment matching as a constrained bipartite assignment problem. Independently bounding the prediction- and ground-truth-side degrees yields four matching strategies: One-to-One, Many-to-One, One-to-Many, and Many-to-Many. We show that the first three are well-defined within the PQ framework, while Many-to-Many falls outside it. These strategies become relevant when instances are fragmented, adjacent objects are difficult to delineate, or annotations are noisy. Central to our framework is a vertex-based accounting of TP, FN, and FP, anchored to ground truth and predicted segments rather than to matching edges. We further show that the framework extends naturally to part-aware panoptic segmentation, and we explore part-aware evaluation on biomedical data. Across configurable case studies we report how different combinations of thresholds and matching strategies behave in practice. We release a unified open-source package built on Panoptica. It exposes Voronoi-based region-wise analysis, part-aware evaluation, and Area Under Threshold Curve computations as configurable options. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31094 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation Großkopf, Erik Kundu, Soumya Snigdha Möller, Hendrik Münster, Nicolas Astaraki, Mehdi Buzduga, Paula Tamara Ritter, Kerstin Wiestler, Benedikt Kirschke, Jan Shapey, Jonathan Vercauteren, Tom Kofler, Florian Computer Vision and Pattern Recognition Artificial Intelligence The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between predicted and ground truth segments, which is only straightforward when the IoU threshold exceeds 0.5. Below 0.5, multiple matching strategies emerge in a poorly explored problem space. We systematically elucidate this space by recasting segment matching as a constrained bipartite assignment problem. Independently bounding the prediction- and ground-truth-side degrees yields four matching strategies: One-to-One, Many-to-One, One-to-Many, and Many-to-Many. We show that the first three are well-defined within the PQ framework, while Many-to-Many falls outside it. These strategies become relevant when instances are fragmented, adjacent objects are difficult to delineate, or annotations are noisy. Central to our framework is a vertex-based accounting of TP, FN, and FP, anchored to ground truth and predicted segments rather than to matching edges. We further show that the framework extends naturally to part-aware panoptic segmentation, and we explore part-aware evaluation on biomedical data. Across configurable case studies we report how different combinations of thresholds and matching strategies behave in practice. We release a unified open-source package built on Panoptica. It exposes Voronoi-based region-wise analysis, part-aware evaluation, and Area Under Threshold Curve computations as configurable options. |
| title | Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.31094 |