English

Cumulative Assessment for Urban 3D Modeling

Computer Vision and Pattern Recognition 2021-07-13 v1

Abstract

Urban 3D modeling from satellite images requires accurate semantic segmentation to delineate urban features, multiple view stereo for 3D reconstruction of surface heights, and 3D model fitting to produce compact models with accurate surface slopes. In this work, we present a cumulative assessment metric that succinctly captures error contributions from each of these components. We demonstrate our approach by providing challenging public datasets and extending two open source projects to provide an end-to-end 3D modeling baseline solution to stimulate further research and evaluation with a public leaderboard.

Keywords

Cite

@article{arxiv.2107.04622,
  title  = {Cumulative Assessment for Urban 3D Modeling},
  author = {Shea Hagstrom and Hee Won Pak and Stephanie Ku and Sean Wang and Gregory Hager and Myron Brown},
  journal= {arXiv preprint arXiv:2107.04622},
  year   = {2021}
}

Comments

Published in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021

R2 v1 2026-06-24T04:03:15.169Z