Deep learning-based 3D image reconstruction and damage mapping using neural radiance fields (Nerfacto)

Source abstract

In structural health monitoring using computer vision, deep learning-based damage identification and three-dimensional (3D) reconstruction of the structure are current hot topics. Traditional photogrammetry techniques are cost-inefficient and time-consuming for 3D reconstruction, and there is no such solid 3D pixelwise damage mapping technique. To overcome these limitations, a new deep neural network (DNN)-based 3D reconstruction method, including damage mapping, is proposed in this article. As the DNN-based 3D reconstruction method, Nerfacto—an advanced version of Neural Radiance Fields models—was selected for achieving high-fidelity 3D reconstruction. This Nerfacto model was modified to create a high-definition 3D reconstruction model of the structure of interest (i.e., a 3-span bridge system). To map damages within the reconstructed 3D model using the modified Nerfacto, the state-of-the-art semantic transformer representation network (STRNet) with test time augmentation (TTA) was also developed for precise pixel-wise crack segmentation. Through extensive case studies, including parametric studies, we found that the modified Nerfacto can learn various appearance features of the structure and generate a very high-definition 3D model. Moreover, the segmented damage (i.e., cracks) from the STRNet with TTA could be mapped onto the reconstructed 3D model. This study demonstrates the potential of combining deep learning with 3D reconstruction for proactive and preventative maintenance strategies, ensuring the safety and longevity of vital structural assets.