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Methods for evaluating the resolution of 3D data derived from satellite images

arXiv 2506.11876 ↗First posted 13 Jun 2025cs.CV eess.IVcited by 0

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Abstract (arXiv)

3D data derived from satellite images is essential for scene modeling applications requiring large-scale coverage or involving locations not accessible by airborne lidar or cameras. Measuring the resolution of this data is important for determining mission utility and tracking improvements. In this work, we consider methods to evaluate the resolution of point clouds, digital surface models, and 3D mesh models. We describe 3D metric evaluation tools and workflows that enable automated evaluation based on high-resolution reference airborne lidar, and we present results of analyses with data of varying quality.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 13 Jun 2025.

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9 of 22 references.

References · 9

  1. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 66
  2. 2022Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Camerascited by 59
  3. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  4. 2020SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimizationcited by 26
  5. 2023Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Cloudscited by 24
  6. 2019Leveraging Vision Reconstruction Pipelines for Satellite Imagerycited by 23
  7. 2024FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitionscited by 5
  8. 2021Cumulative Assessment for Urban 3D Modelingcited by 3
  9. 2024BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modellingcited by 2

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