mlsanityData to 14 Oct 2026

Cumulative Assessment for Urban 3D Modeling

arXiv 2107.04622 ↗First posted 9 Jul 2021cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

6 of 15 references.

References · 6

  1. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
  2. 2018SpaceNet: A Remote Sensing Dataset and Challenge Seriescited by 179
  3. 2018Semantic Stereo for Incidental Satellite Imagescited by 53
  4. 2019Leveraging Vision Reconstruction Pipelines for Satellite Imagerycited by 23
  5. 2019A New Stereo Benchmarking Dataset for Satellite Imagescited by 15
  6. 2019Primitive-based 3D Building Modeling, Sensor Simulation, and Estimationcited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.