Research map / Learning theory and algorithms / Combinatorial and search optimization
An Exact Penalty Method for Locally Convergent Maximum Consensus
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply exact-penalty Frank-Wolfe refinement initialized from RANSAC | Maximum-consensus estimation · Fundamental matrix, homography, affinity and triangulation benchmarks | consensus size | Helped |
| Apply exact-penalty Frank-Wolfe refinement initialized from least squares | Linear regression on unbalanced synthetic data · Linear regression | consensus size | Helped |
| Use exact-penalty refinement instead of randomized RANSAC variants | Maximum-consensus estimation · High outlier rates | runtime | Mixed |
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