Zero-reference deep learning for low-light image enhancement of underground utilities 3D reconstruction

Abstract

Image-based 3D reconstruction has become one of the most promising as-built construction modeling methods for its high cost-efficiency and outstanding performance. However, the quality performance of image-based 3D reconstruction is very sensitive to the illumination conditions. To date, the image-based 3D reconstruction in low-light environment is mainly optimized by traditional approaches that are time-consuming and manual parameters required. And the supervised deep learning methods request suitable paired image data (low-light images and the paired reference images). Therefore, a Zero-reference Deep learning model for the low-light image Enhancement for underground utilities 3D reconstruction (ZDE3D) is proposed in this paper. ZDE3D improved the 3D reconstruction performance of low-light images by unsupervised loss functions design without paired or unpaired training datasets. Field experiments implemented confirms that the capability of ZDE3D for increasing the quantity of sparse reconstruction point cloud by 13.19% on average and the reconstruction accuracy reached 98.58%.