# How is epipolar (Sampson) error computed on a generated clip, what does it say about geometry versus texture, where does it break for moving subjects, and what do papers report for named models?

- id: art_01M5MC996MDG19RBCX95HYVK5E
- 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: 2026-10-23T12:38:00.144Z
- url: https://veyrul.com/articles/art_01M5MC996MDG19RBCX95HYVK5E

Epipolar error needs no learned reconstruction: match SIFT features between sampled frames, fit a fundamental matrix with the normalized 8-point algorithm in RANSAC, and average the Sampson distance of the matches to their epipolar lines; lower means the pair obeys one rigid camera motion. The Epipolar-Geometry paper reports 0.190 for a Wan2.1-1.3B baseline versus 0.131 after preference finetuning on Sampson-ranked pairs, with held-out-frame splat fidelity and human 'consistent' rates moving the same way (54.1% to 71.8%). It assumes a static scene (the authors say moving objects corrupt it), can be fooled by repetitive texture or a near-static clip, and GeoFlow shows a masked, short-stride variant for dynamic clips.

Epipolar error needs no learned reconstruction: match SIFT features between sampled frames, fit a fundamental matrix with the normalized 8-point algorithm in RANSAC, and average the Sampson distance of the matches to their epipolar lines; lower means the pair obeys one rigid camera motion. The Epipolar-Geometry paper reports 0.190 for a Wan2.1-1.3B baseline versus 0.131 after preference finetuning on Sampson-ranked pairs, with held-out-frame splat fidelity and human 'consistent' rates moving the same way (54.1% to 71.8%). It assumes a static scene (the authors say moving objects corrupt it), can be fooled by repetitive texture or a near-static clip, and GeoFlow shows a masked, short-stride variant for dynamic clips.

Curator qualifications: Epipolar (Sampson) error needs no learned model: SIFT matches, 8-point RANSAC, mean Sampson distance.

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/2510.21615v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2605.18365v1",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Epipolar error needs no learned reconstruction: match SIFT features between sampled frames, fit a fundamental matrix with the normalized 8-point algorithm in RANSAC, and average the Sampson distance of the matches to their epipolar lines; lower means the pair obeys one rigid camera motion. The Epipolar-Geometry paper reports 0.190 for a Wan2.1-1.3B baseline versus 0.131 after preference finetuning on Sampson-ranked pairs, with held-out-frame splat fidelity and human 'consistent' rates moving the same way (54.1% to 71.8%). It assumes a static scene (the authors say moving objects corrupt it), can be fooled by repetitive texture or a near-static clip, and GeoFlow shows a masked, short-stride variant for dynamic clips. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2510.21615v2

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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