@inproceedings{bdb24d5439af4926836d1150acfddd9a,
title = "Comparison of 3D Reconstruction between Neural Radiance Fields and Structure-from-Motion-Based Photogrammetry from 360° Videos",
abstract = "Imagery is a standard modality of visual data capture on construction sites for documenting construction progress. Assimilating the data from multiple disjointed 2D images into a single 3D format enhances visualization and scene understanding and increases the data usability for tasks like quantity estimation and progress tracking. Two popular methods for 3D reconstruction are structure-from-motion (SfM)-based photogrammetry and neural radiance fields (NeRF), a neural network-based technique in computer vision. In this paper, we compare the spatial geometric accuracy of 3D reconstruction from 360° videos of construction sites using the SfM library called Colmap and NeRF. Our experiments show that 3D reconstruction from conventional photogrammetry is sharper than NeRF and more accurate in capturing object details and boundaries.",
author = "Mohit Gupta and Andre Borrmann and Thomas Czerniawski",
note = "Publisher Copyright: {\textcopyright} International Conference on Computing in Civil Engineering 2023.All rights reserved.; ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023 ; Conference date: 25-06-2023 Through 28-06-2023",
year = "2024",
doi = "10.1061/9780784485224.052",
language = "English",
series = "Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "429--436",
editor = "Yelda Turkan and Joseph Louis and Fernanda Leite and Semiha Ergan",
booktitle = "Computing in Civil Engineering 2023",
}