SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911642086801408 |
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| author | Kellner, Maximilian Cervantes, Mariana Ferrandon Pan, Yuandong Lu, Ruodan Brilakis, Ioannis Reiterer, Alexander |
| author_facet | Kellner, Maximilian Cervantes, Mariana Ferrandon Pan, Yuandong Lu, Ruodan Brilakis, Ioannis Reiterer, Alexander |
| contents | We propose a novel dataset that has been specifically designed for 3D semantic segmentation of bridges and the domain gap analysis caused by varying sensors. This addresses a critical need in the field of infrastructure inspection and maintenance, which is essential for modern society. The dataset comprises high-resolution 3D scans of a diverse range of bridge structures from various countries, with detailed semantic labels provided for each. Our initial objective is to facilitate accurate and automated segmentation of bridge components, thereby advancing the structural health monitoring practice. To evaluate the effectiveness of existing 3D deep learning models on this novel dataset, we conduct a comprehensive analysis of three distinct state-of-the-art architectures. Furthermore, we present data acquired through diverse sensors to quantify the domain gap resulting from sensor variations. Our findings indicate that all architectures demonstrate robust performance on the specified task. However, the domain gap can potentially lead to a decline in the performance of up to 11.4% mIoU. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis Kellner, Maximilian Cervantes, Mariana Ferrandon Pan, Yuandong Lu, Ruodan Brilakis, Ioannis Reiterer, Alexander Computer Vision and Pattern Recognition We propose a novel dataset that has been specifically designed for 3D semantic segmentation of bridges and the domain gap analysis caused by varying sensors. This addresses a critical need in the field of infrastructure inspection and maintenance, which is essential for modern society. The dataset comprises high-resolution 3D scans of a diverse range of bridge structures from various countries, with detailed semantic labels provided for each. Our initial objective is to facilitate accurate and automated segmentation of bridge components, thereby advancing the structural health monitoring practice. To evaluate the effectiveness of existing 3D deep learning models on this novel dataset, we conduct a comprehensive analysis of three distinct state-of-the-art architectures. Furthermore, we present data acquired through diverse sensors to quantify the domain gap resulting from sensor variations. Our findings indicate that all architectures demonstrate robust performance on the specified task. However, the domain gap can potentially lead to a decline in the performance of up to 11.4% mIoU. |
| title | SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.15369 |