Urban Semantic 3D Reconstruction From Multiview Satellite Imagery

Matthew J. Leotta, Chengjiang Long, Bastien Jacquet, Matthieu Zins, Dan Lipsa, Jie Shan, Bo Xu, Zhixin Li, Xu Zhang, Shih-Fu Chang, Matthew Purri, Jia Xue, Kristin Dana; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0


Methods for automated 3D urban modeling typically result in very dense point clouds or surface meshes derived from either overhead lidar or imagery (multiview stereo). Such models are very large and have no semantic separation of individual structures (i.e. buildings, bridges) from the terrain. Furthermore, such dense models often appear "melted" and do not capture sharp edges. This paper demonstrates an end-to-end system for segmenting buildings and bridges from terrain and estimating simple, low polygon, textured mesh models of these structures. The approach uses multiview-stereo satellite imagery as a starting point, but this work focuses on segmentation methods and regularized 3D surface extraction. Our work is evaluated on the IARPA CORE3D public data set using the associated ground truth and metrics. A web-based application deployed on AWS runs the algorithms and provides visualization of the results. Both the algorithms and web application are provided as open source software as a resource for further research or product development.

Related Material

author = {Leotta, Matthew J. and Long, Chengjiang and Jacquet, Bastien and Zins, Matthieu and Lipsa, Dan and Shan, Jie and Xu, Bo and Li, Zhixin and Zhang, Xu and Chang, Shih-Fu and Purri, Matthew and Xue, Jia and Dana, Kristin},
title = {Urban Semantic 3D Reconstruction From Multiview Satellite Imagery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}