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.

翻译：我们提出了一种专门为桥梁三维语义分割及由不同传感器引起的领域间隙分析而设计的新型数据集。这满足了基础设施检测与维护领域的关键需求，对现代社会至关重要。该数据集包含来自多个国家多种桥梁结构的高分辨率三维扫描，并为每个扫描提供了详细的语义标注。我们的首要目标是促进桥梁构件的精确自动化分割，从而推动结构健康监测实践的发展。为评估现有三维深度学习模型在此新数据集上的有效性，我们对三种不同的前沿架构进行了全面分析。此外，我们展示了通过多种传感器采集的数据，以量化由传感器差异导致的领域间隙。研究结果表明，所有架构在指定任务上均表现出稳健的性能。然而，领域间隙可能导致性能下降高达11.4%的平均交并比（mIoU）。