构建桥梁3D语义分割数据集并分析传感器差异带来的性能影响
SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis
- 专为桥梁3D语义分割设计，涵盖多国多样结构的高精度扫描数据
- 不同传感器导致性能下降最高达11.4% mIoU，凸显域差距问题
- 适用于基础设施检测、结构健康监测及跨传感器模型评估
我们提出一个新型数据集，专门用于桥梁的3D语义分割及由传感器差异引起的域差距分析。该数据集涵盖来自多个国家的多种桥梁结构的高分辨率3D扫描，并对每个部分提供详细语义标注。其目标是推动桥梁部件的精准自动化分割，从而提升结构健康监测水平。为评估现有3D深度学习模型在此数据集上的表现，我们对三种先进架构进行了全面分析。此外，我们还引入了不同传感器采集的数据，量化传感器差异带来的域差距。结果表明，所有模型在该任务上均表现出稳健性能，但域差距可能导致性能下降最多达11.4% mIoU。
原文摘要 · Abstract (English)
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.
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