CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911024564666368 |
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| author | Lorenz, Julian Phatak, Mrunmai Schön, Robin Ludwig, Katja Hörmann, Nico Friedrich, Annemarie Lienhart, Rainer |
| author_facet | Lorenz, Julian Phatak, Mrunmai Schön, Robin Ludwig, Katja Hörmann, Nico Friedrich, Annemarie Lienhart, Rainer |
| contents | 2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcome this data bottleneck, we present CoPa-SG, a synthetic scene graph dataset with highly precise ground truth and exhaustive relation annotations between all objects. Moreover, we introduce parametric and proto-relations, two new fundamental concepts for scene graphs. The former provides a much more fine-grained representation than its traditional counterpart by enriching relations with additional parameters such as angles or distances. The latter encodes hypothetical relations in a scene graph and describes how relations would form if new objects are placed in the scene. Using CoPa-SG, we compare the performance of various scene graph generation models. We demonstrate how our new relation types can be integrated in downstream applications to enhance planning and reasoning capabilities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_21357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations Lorenz, Julian Phatak, Mrunmai Schön, Robin Ludwig, Katja Hörmann, Nico Friedrich, Annemarie Lienhart, Rainer Computer Vision and Pattern Recognition 2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcome this data bottleneck, we present CoPa-SG, a synthetic scene graph dataset with highly precise ground truth and exhaustive relation annotations between all objects. Moreover, we introduce parametric and proto-relations, two new fundamental concepts for scene graphs. The former provides a much more fine-grained representation than its traditional counterpart by enriching relations with additional parameters such as angles or distances. The latter encodes hypothetical relations in a scene graph and describes how relations would form if new objects are placed in the scene. Using CoPa-SG, we compare the performance of various scene graph generation models. We demonstrate how our new relation types can be integrated in downstream applications to enhance planning and reasoning capabilities. |
| title | CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.21357 |