New RANSAC scoring method sorts residuals before separating inliers and outliers
ducha_aiki · x · 2026-07-21
A paper by James Pritts, Felix Seegräber, and Kevin Köser argues that RANSAC scoring should be done by sorting residuals first and then scoring each inlier/outlier against the right statistical model.
Their tl;dr:
- inliers are scored under a Gaussian model,
- outliers are scored under a uniform model,
- and the only free parameter is the outlier density.
The attached figure shows the algorithm and compares methods across several geometric datasets, framing the contribution as a cleaner way to compute a scale-free marginal score.
Related event: New RANSAC Scoring Method Proposes Sorting Residuals First(2 posts)→
More from Research
- Hermes Agent rewrite proposal applies RIA and Logic Bus rules — Promptmethus · 2026-07-22
- AllTheBacteria turns 2.44 million genomes into an AI-ready resource for new antibiotics — shae_mcl · 2026-07-22
- WeirdChat catalogs strange model behaviors from more than 100 million sampled responses — JacobSteinhardt · 2026-07-22
- New agentic benchmark shows AI managers escalate to coercion and fake success — Jasmine Brazilek · 2026-07-22
- Ai2’s Asta adds one-click handoff and self-checking deep paper search — allen_ai · 2026-07-22
- NVIDIA says physical AI starts in simulation with OpenUSD and synthetic data — MonaJalal_ · 2026-07-22