mlsanityData to 7 Oct 2026

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

arXiv 2607.00417 ↗First posted 1 Jul 2026cs.CV cs.AIcited by 1

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Abstract (arXiv)

In the era of satellite constellations, multi-view optical satellite imagery is pivotal for Earth Observation (EO) and high-quality Digital Surface Model (DSM) reconstruction. Although feed-forward 3D foundation models have transformed computer vision, their deployment in satellite remote sensing is inherently constrained by the structural discrepancy between implicit perspective assumptions and explicit orbital pushbroom geometry. This geometric incongruity is further compounded by pronounced view-set heterogeneity. We present EO-VGGT, a framework that adapts a frozen perspective-driven model to orbital observations via explicit physical geometry embedding.First, the Geometry-Correlation Constrained Selection (GCCS) strategy prunes sub-optimal observations by balancing geometric diversity and radiometric consistency to optimize the input sequence. Second, a Sensor-Ray Encoder (SRE) parameterizes pixel-level pushbroom lines of sight derived from the Rational Function Model (RFM) into high-dimensional space-geometric tokens, reconciling the mathematical discrepancy between central projection and orbital kinematics. Third, a lightweight Ray-Pointing-Aware Adapter (RPAA) employs gated residual blocks to integrate these tokens directly into the frozen transformer backbone. Our findings underscore that integrating explicit physical geometry with optimized view selection is essential for robust feed-forward satellite 3D reconstruction.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

17 of 35 references.

References · 17

  1. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
  2. 2025MapAnything: Universal Feed-Forward Metric 3D Reconstructioncited by 295
  3. 2025π3: Permutation-Equivariant Visual Geometry Learningcited by 292
  4. 2018Semantic Stereo for Incidental Satellite Imagescited by 53
  5. 2023Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairscited by 29
  6. 2019Leveraging Vision Reconstruction Pipelines for Satellite Imagerycited by 23
  7. 2024Gaussian Splatting for Efficient Satellite Image Photogrammetrycited by 15
  8. 2021Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matchingcited by 14
  9. 2024SAT-NGP : Unleashing Neural Graphics Primitives for Fast Relightable Transient-Free 3D reconstruction from Satellite Imagerycited by 14
  10. 2025SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Imagescited by 13
  11. 20243D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisionscited by 12
  12. 2021ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Imagescited by 10
5 more
  1. 2025Seeing through Satellite Images at Street Viewscited by 8
  2. 2025Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervisioncited by 6
  3. 2025An Evaluation of DUSt3R/MASt3R/VGGT 3D Reconstruction on Photogrammetric Aerial Blockscited by 5
  4. 2024Comparative Analysis of Advanced Feature Matching Algorithms in Challenging High Spatial Resolution Optical Satellite Stereo Scenarioscited by 3
  5. 2026Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagerycited by 1

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