NeRF-Con: Neural Radiance Fields for Automated Construction Progress Monitoring

The monitoring of construction progress is crucial for ensuring project timelines, budget adherence, and quality control. Traditional methods often involve manual inspection, which is labor-intensive and prone to human error. We introduce NeRF-Con, an innovative approach utilizing Neural Radiance Fields (NeRF) to automate the process of construction progress monitoring. NeRF-Con can infer images that render the construction site with a level of quality comparable to reality by utilizing NeRF, which synthesizes novel views of complex scenes from a sparse set of images. Additionally, by employing a segmentation model, NeRF-Con can compare these rendered images with BIM to evaluate the progress of the work. This capability is achieved by training the model using handheld smartphone-captured video. This paper details a method for applying NeRF in real construction sites with data collection, pre-processing, and progress evaluation. In assessing the model’s performance, comparisons are made with data from mobile-LiDAR, stand-LiDAR, and BIM. With this research, we suggest potential future studies in applying NeRF models to construction progress monitoring systems.