🤖 AI Summary
To address cross-sensor domain shift in bridge 3D semantic segmentation, this paper introduces the first bridge-specific 3D semantic segmentation benchmark for structural health monitoring, comprising high-precision LiDAR and photogrammetric scans from multiple countries, with fine-grained component-level annotations. We conduct cross-sensor generalization evaluation using three state-of-the-art models—PointPillars, KPConv, and SPVCNN—and quantitatively demonstrate, for the first time, that sensor-induced domain shift degrades mIoU by up to 11.4%. We further propose a reproducible empirical framework for domain shift assessment. Our key contributions are: (1) establishing the first dedicated 3D semantic segmentation dataset for bridge structures; (2) systematically characterizing the extent of sensor-induced domain shift; and (3) providing a standardized benchmark to advance intelligent bridge condition diagnosis and domain adaptation algorithm development.
📝 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.