Abstract
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.
Abstract (translated)
我们提出了一套专门为桥梁的三维语义分割和由不同传感器引起的域差距分析而设计的新数据集。这满足了基础设施检测与维护领域的一个关键需求，这对于现代社会至关重要。该数据集包含来自多个国家各种不同类型桥梁的高分辨率3D扫描图像，并为每一种提供了详细的语义标签。我们的初步目标是促进桥梁组件的精确和自动化分割，从而推进结构健康监测实践的发展。 为了评估现有三维深度学习模型在这套新数据集上的有效性，我们对三种不同的最先进的架构进行了全面分析。此外，我们还通过不同类型的传感器获取了数据，以量化由于传感器变化引起的域差距。我们的研究结果表明，所有架构在指定任务上都表现出稳健的性能，但域差距可能导致高达11.4% mIoU（平均交并比）的性能下降。
URL
https://arxiv.org/abs/2512.15369
