Research map / Learning theory and algorithms / Matrix and tensor methods
Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use VarPro instead of Joint optimization | affine bundle adjustment · Dinosaur (trimmed) | success rate (%): 4 -> 94 | Helped |
| Use VarPro instead of Joint optimization | affine bundle adjustment · Corridor | success rate (%): 40 -> 100 | Helped |
| Use VarPro instead of Joint optimization | affine bundle adjustment · Dinosaur including outliers | success rate (%): 0 -> 100 | Helped |
| Use VarPro instead of Joint optimization | affine bundle adjustment · Venice (skeleton) | success rate (%): 0 -> 80 | Helped |
| Add embedded point iterations to Joint (ablation) | Joint optimization · trimmed dinosaur sequence | success rate (%): (4%) -> (24% ) | Helped |
| Set the v-variable damping λv to zero (ablation) | Joint optimization without EPI · trimmed dinosaur sequence | success rate (%): (4%) -> (0%) | Hurt |
| Disable damping of v and optimize v for each updated u (VarPro) (ablation) | Joint+EPI · trimmed dinosaur sequence | success rate (%): (24% ) -> (94 %) | Helped |
| Use VarPro-MINRES rather than direct VarPro | affine bundle adjustment · Dinosaur (trimmed) | success rate (%): 94 -> 99 | Helped |
Closest papers by meaning
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- pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment (CVPR 2018 · similarity 0.83)
- Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming (NeurIPS 2024 · similarity 0.81)
- Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World (CVPR 2025 · similarity 0.80)
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