# How far can I trust a MEt3R-style score that rests on a learned reconstruction backbone: what do controlled-corruption, injected-warp and human-ranking tests say, and what do COLMAP-based scores do better or worse?

- id: art_01M5MC9ECW5YCVKMG13ARBMVXZ
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T06, T15, T20 · tags: none
- updated: 2026-10-23T12:38:05.463Z
- url: https://veyrul.com/articles/art_01M5MC9ECW5YCVKMG13ARBMVXZ

Three independent tests probe MEt3R from outside. SysCON3D (v1) finds that learned backbones (DUSt3R, MASt3R, VGGT, Fast3R) produce geometry for unrelated or noise inputs, MEt3R scores such sets as more consistent than real ones in its harness (negative effect sizes, 23% overall win rate), and its rank correlation with humans on eight sparse-view NVS methods is -0.10 to 0.26 against 0.98 for a COLMAP-based score. GeCo's injected-warp test finds MEt3R near chance at spotting a deformed frame. The SGC paper, in a different setup (text/video generators, 11 entities), reports MEt3R at Spearman 0.864 with humans. The sources agree that MEt3R is no stand-alone gate; they disagree on how well it ranks generators.

Three independent tests probe MEt3R from outside. SysCON3D (v1) finds that learned backbones (DUSt3R, MASt3R, VGGT, Fast3R) produce geometry for unrelated or noise inputs, MEt3R scores such sets as more consistent than real ones in its harness (negative effect sizes, 23% overall win rate), and its rank correlation with humans on eight sparse-view NVS methods is -0.10 to 0.26 against 0.98 for a COLMAP-based score. GeCo's injected-warp test finds MEt3R near chance at spotting a deformed frame. The SGC paper, in a different setup (text/video generators, 11 entities), reports MEt3R at Spearman 0.864 with humans. The sources agree that MEt3R is no stand-alone gate; they disagree on how well it ranks generators.

Curator qualifications: External tests find learned backbones produce geometry for unrelated or noise inputs and MEt3R can score such sets as more consistent than real ones in one harness.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/html/2605.18754v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2603.19048v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2512.22274v5",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Three independent tests probe MEt3R from outside. SysCON3D (v1) finds that learned backbones (DUSt3R, MASt3R, VGGT, Fast3R) produce geometry for unrelated or noise inputs, MEt3R scores such sets as more consistent than real ones in its harness (negative effect sizes, 23% overall win rate), and its rank correlation with humans on eight sparse-view NVS methods is -0.10 to 0.26 against 0.98 for a COLMAP-based score. GeCo's injected-warp test finds MEt3R near chance at spotting a deformed frame. The SGC paper, in a different setup (text/video generators, 11 entities), reports MEt3R at Spearman 0.864 with humans. The sources agree that MEt3R is no stand-alone gate; they disagree on how well it ranks generators. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2605.18754v1

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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