A 2D–3D Point Cloud Fusion Method for Spatial Morphology Identification and Measurement of Structural Damage

Abstract

As bridges age, timely and accurate identification and quantification of cracks and structural defects are essential for ensuring safety and durability. Existing 2D image-based methods are limited by the lack of reliable depth information, hindering precise description of defect shape, severity, and evolution. In contrast, 3D point cloud data provides high-resolution spatial geometric information, supporting more accurate defect analysis. This paper proposes a scale-adaptive cross-modal defect detection and quantification method that integrates 2D image data with 3D point cloud information. First, a 3D semantic segmentation model with a cross-modal registration strategy enables precise segmentation of large structural cracks, while an image-guided fusion mechanism facilitates accurate depth map generation for micro-cracks. Additionally, a 2D segmentation model-guided defect 3D point cloud segmentation algorithm is proposed for detecting small durability cracks. Finally, a 3D defect quantification framework is developed to systematically measure crack length, width, depth, and volume, providing multi-dimensional indicators for structural assessment. Experimental results show that the proposed cross-modal fusion approach significantly outperforms single-modality methods. By integrating PointNet++ and U-Net, the system achieves high-precision multi-scale segmentation with an average mIoU of 0.87–0.89. Furthermore, the framework enables reliable 3D quantification of crack length, width, depth, and volume, providing a more comprehensive and objective basis for structural health assessment than traditional 2D methods. This research provides an effective approach for overcoming the limitations of 2D methods, offering a reliable solution for bridge structural health monitoring and durability prediction.