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Title	3D Urban Building Reconstruction Using Neural Rendering Technique
ID_Doc	187
Authors	Kim C.; Lee J.; Choi W.; Kwon Y.; Sohn H.-G.
Year	2024
Published	Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023
DOI	http://dx.doi.org/10.1061/9780784485224.005
Abstract	The growing emphasis on smart cities and digital twins has heightened the significance of 3D geospatial information. However, 3D urban building modeling, considered a significant object of interest, typically involves time-consuming manual labor. Several studies have been conducted to reconstruct 3D urban buildings using multidimensional and multitemporal. However, several constraints, such as data availability, view angles, occlusions, and texture detail, affect the accuracy and completeness of 3D models. Furthermore, inconsistencies across different datasets pose difficulties in comparing results across studies and developing generalized methods. In this study, our objective aims to utilize neural rendering techniques to reconstruct a 3D model of buildings in urban areas using imagery captured from unmanned aerial vehicle (UAV). Neural radiance field (NeRF) synthesized a continuous scene of unknown direction for areas not covered by the UAV images. As a result, the proposed method provided a more comprehensive view to supplement the occlusions in the photogrammetry-based point cloud. The results were quantitatively evaluated and indicated that the proposed method could be a feasible complementary solution for photogrammetry-based 3D building modeling. © International Conference on Computing in Civil Engineering 2023.All rights reserved.
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