mlsanityData to 14 Oct 2026

Why 3D Reconstruction Breaks Down on Under-Ice AUV Video, and How to Predict It

arXiv 2610.19993 ↗First posted 14 Oct 2026cs.CV cs.ROcited by 0

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Abstract (arXiv)

Reconstruction of 3D structures using under-ice AUV video footage has been inaccurate, although it is not certain whether this is due to shortcomings in the methods or an inherent deficiency of the capture system. We test four different models for reconstruction on the same Antarctic under-ice AUV video (Icefin, Kamb Ice Stream): classical feature matching SfM, differentiable photometric splatting, volumetric depth fusion, and a pose-free feed-forward foundation model. All four fail, each in a distinct way: near-total registration yielding almost no structure, geometrically broken output despite healthy photometric metrics, a persistent coverage artifact, and unstable cross-chunk scale. We show that all four are consistent with the same capture regime, a small camera-baseline-to-scene-depth ratio. Following our analysis, we propose a detection system that flags this type of failure in minutes, rather than hours of wasted compute.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 14 Oct 2026.

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5 of 18 references.

References · 5

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
  3. 2025Depth Anything 3: Recovering the Visual Space from Any Viewscited by 692
  4. 2023Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mappingcited by 3
  5. 2025UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splattingcited by 2

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