SemanticBridge—A dataset for 3D semantic segmentation of bridges and domain gap analysis

This paper proposes 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. The 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, a comprehensive analysis of three distinct state-of-the-art architectures is conducted. Additionally, data was acquired through various sensors to quantify the domain gap resulting from sensor variations. The 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. Code and data are available at https://github.com/mvg-inatech/3d_bridge_segmentation . • The introduction of an enriched dataset of bridges collected with Terrestrial Laser Scanners and Mobile Laser Scanners to serve as a uniform benchmark. • An investigation into the domain gap in 3D semantic segmentation, which arises from the use of disparate data capture sensors. • A general comparative evaluation of the performance among three state-of- the-art architectures in the bridges semantic segmentation task.