SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis

TL;DR

This paper introduces a new 3D bridge dataset with semantic labels to improve infrastructure inspection, analyzing model performance and sensor-induced domain gaps.

Contribution

It provides a specialized dataset for 3D bridge segmentation and evaluates the impact of sensor variations on model performance.

Findings

All tested architectures perform robustly on the dataset.

Sensor variations can cause up to 11.4% decline in mIoU.

The dataset enables domain gap analysis for bridge inspection models.

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.