Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Using 2D scene graphs as an enabler for DT topology
https://orcid.org/0009-0004-0091-3691
https://orcid.org/0009-0008-6333-9542
https://orcid.org/0009-0003-9249-0464
https://orcid.org/0000-0001-5246-7727
https://orcid.org/0009-0009-6619-3742
Scan-to-BIM research has gained significant attention, yet many reconstruction methodologies overlook the crucial topological relationships in Building Information Modeling (BIM). To address this challenge, we propose using Scene Graphs to capture contextual relationships in images, focusing on hallways and offices at the Technical University of Munich (TUM). Our approach involves fine-tuning an existing Scene Graph Generation (SGG) model and proposing an in-house model both aiming to predict scene graphs, by integrating object detection and predicate detection methods. The fine-tuned SGG model with the benchmark archived the recall@50 of 95.57 in predicate classification mode. Comparatively, our in-house model attained a recall of 65.12 for the overall scene graph generation. Despite these promising results, some limitations remain, such as low object detection accuracy and the exclusion of non-relationships, as well as the evaluation being limited to qualitative comparisons and was done independently for each method. These limitations highlight areas for future work. Nevertheless, this study offers a proof of concept for integrating scene graph predictions into Scan-to-BIM workflows while identifying areas for further improvement.
Using 2D scene graphs as an enabler for DT topology
https://orcid.org/0009-0004-0091-3691
https://orcid.org/0009-0008-6333-9542
https://orcid.org/0009-0003-9249-0464
https://orcid.org/0000-0001-5246-7727
https://orcid.org/0009-0009-6619-3742
Scan-to-BIM research has gained significant attention, yet many reconstruction methodologies overlook the crucial topological relationships in Building Information Modeling (BIM). To address this challenge, we propose using Scene Graphs to capture contextual relationships in images, focusing on hallways and offices at the Technical University of Munich (TUM). Our approach involves fine-tuning an existing Scene Graph Generation (SGG) model and proposing an in-house model both aiming to predict scene graphs, by integrating object detection and predicate detection methods. The fine-tuned SGG model with the benchmark archived the recall@50 of 95.57 in predicate classification mode. Comparatively, our in-house model attained a recall of 65.12 for the overall scene graph generation. Despite these promising results, some limitations remain, such as low object detection accuracy and the exclusion of non-relationships, as well as the evaluation being limited to qualitative comparisons and was done independently for each method. These limitations highlight areas for future work. Nevertheless, this study offers a proof of concept for integrating scene graph predictions into Scan-to-BIM workflows while identifying areas for further improvement.
Using 2D scene graphs as an enabler for DT topology
https://orcid.org/0009-0004-0091-3691
https://orcid.org/0009-0008-6333-9542
https://orcid.org/0009-0003-9249-0464
https://orcid.org/0000-0001-5246-7727
https://orcid.org/0009-0009-6619-3742
Scan-to-BIM research has gained significant attention, yet many reconstruction methodologies overlook the crucial topological relationships in Building Information Modeling (BIM). To address this challenge, we propose using Scene Graphs to capture contextual relationships in images, focusing on hallways and offices at the Technical University of Munich (TUM). Our approach involves fine-tuning an existing Scene Graph Generation (SGG) model and proposing an in-house model both aiming to predict scene graphs, by integrating object detection and predicate detection methods. The fine-tuned SGG model with the benchmark archived the recall@50 of 95.57 in predicate classification mode. Comparatively, our in-house model attained a recall of 65.12 for the overall scene graph generation. Despite these promising results, some limitations remain, such as low object detection accuracy and the exclusion of non-relationships, as well as the evaluation being limited to qualitative comparisons and was done independently for each method. These limitations highlight areas for future work. Nevertheless, this study offers a proof of concept for integrating scene graph predictions into Scan-to-BIM workflows while identifying areas for further improvement.
Using 2D scene graphs as an enabler for DT topology
Kim, Nayun (Autor:in) / Atacan, Kural Avgoren (Autor:in) / Alrabab’h, Mohammad Adnan Mohammad (Autor:in) / Collins, Fiona (Autor:in) / Du, Changyu (Autor:in) / TUHH Universitätsbibliothek (Gastgebende Institution)
2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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