Research map / 3D vision / Depth and stereo
A New Rank Constraint on Multi-View Fundamental Matrices, and Its Application to Camera Location Recovery
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the multiview rank constraint with IRLS-ADMM to pairwise essential matrices | LUD essential matrix estimates · Real image collections, 50 images | median essential matrix error: 17.69% | Helped |
| Apply the multiview rank constraint with IRLS-ADMM to pairwise essential matrices | LUD essential matrix estimates · Real image collections, 150 images | median essential matrix error: 6.68% | Helped |
| Use rank-constrained essential matrix estimates to recover camera locations | LUD camera-location pipeline · Real image collections, 50 images | median camera location error: 19.73% | Helped |
| Use rank-constrained essential matrix estimates to recover camera locations | LUD camera-location pipeline · Real image collections, 150 images | median camera location error: 8.77% | Helped |
| Initialize bundle adjustment with parameters from our method rather than LUD | Bundle adjustment · Real image collections, 50, 100, and 150 images | median translation error: 11.52%, 3.13% and 5.43% | Helped |
| Recover fundamentals jointly rather than separately averaging rotation and translation | Global SfM camera geometry estimation · Real image collections | camera parameter recovery accuracy | Helped |
| Use the proposed multiview rank constraints on fewer images | Our method · Real image collections, 50 vs. 150 images | relative improvement in median essential matrix error over LUD: 17.69%; 6.68% | Mixed |
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