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

Yang Su, Jun Wang, Xiangyu Wang, Lei Hu, Yuan Yao, Wenchi Shou, Danqi Li

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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%.
Original languageEnglish
Article number104930
Number of pages12
JournalAutomation in Construction
Volume152
DOIs
Publication statusPublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Deep learning
  • Low-light reconstruction
  • As-built record
  • Image-based 3D reconstruction
  • As-built underground utilities
  • Image enhancement

Fingerprint

Dive into the research topics of 'Zero-reference deep learning for low-light image enhancement of underground utilities 3D reconstruction'. Together they form a unique fingerprint.

Cite this