Research map / Learning theory and algorithms / Combinatorial and search optimization
A Robust and Efficient Algorithm for the PnL Problem Using Algebraic Distance to Approximate the Reprojection Distance
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add second-step damped Newton on algebraic distance 2 after the first step (ablation) | OAPnL I · real data TFH | rotation error (deg): 0.60 -> 0.10 | Helped |
| add second-step damped Newton on algebraic distance 2 after the first step (ablation) | OAPnL I · real data TFH | translation error (m): 0.48 -> 0.07 | Helped |
| add second-step damped Newton on algebraic distance 2 after the first step (ablation) | OAPnL I · real data BB | rotation error (deg): 0.18 -> 0.18 | No clear effect |
| add second-step damped Newton on algebraic distance 2 after the first step (ablation) | OAPnL I · real data MC1 | translation error (m): 0.01 -> 0.003 | Helped |
| replace second algebraic distance step with LM on the exact reprojection cost (ablation) | OAPnL II · real data TFH | rotation error (deg): 0.10 -> 0.09 | No clear effect |
| replace second algebraic distance step with LM on the exact reprojection cost (ablation) | OAPnL II · synthetic planar, large noise | pose error | Mixed |
| use algebraic distance 2 instead of reprojection LM (ablation) | OAPnL · computation time | runtime | Helped |
| proposed first-step algebraic distance with hidden variable solver vs OPnPL | OAPnL I · real data TFH | rotation error (deg): 0.42 -> 0.60 | Hurt |
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