{"ID":2900970,"CreatedAt":"2026-06-01T05:51:17.9442275Z","UpdatedAt":"2026-06-01T09:30:02.809313052Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2605.31094","arxiv_id":"2605.31094","title":"Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation","abstract":"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.","short_abstract":"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 strateg...","url_abs":"https://arxiv.org/abs/2605.31094","url_pdf":"https://arxiv.org/pdf/2605.31094v1","authors":"[\"Erik Großkopf\",\"Soumya Snigdha Kundu\",\"Hendrik Möller\",\"Nicolas Münster\",\"Mehdi Astaraki\",\"Paula Tamara Buzduga\",\"Kerstin Ritter\",\"Benedikt Wiestler\",\"Jan Kirschke\",\"Jonathan Shapey\",\"Tom Vercauteren\",\"Florian Kofler\"]","published":"2026-05-29T10:04:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}