The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTs
About
We present a simple baseline for directly estimating the relative pose (rotation and translation, including scale) between two images. Deep methods have recently shown strong progress but often require complex or multi-stage architectures. We show that a handful of modifications can be applied to a Vision Transformer (ViT) to bring its computations close to the Eight-Point Algorithm. This inductive bias enables a simple method to be competitive in multiple settings, often substantially improving over the state of the art with strong performance gains in limited data regimes.
Chris Rockwell, Justin Johnson, David F. Fouhey• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Rotation Estimation | SUN360 Large Overlap | Geodesic Error (Mean)22.33 | 13 | |
| Pose Estimation | ACID Small | Rotation Avg Error (°)8.466 | 7 | |
| Pose Estimation | ACID Large | Rotation Avg Error (°)2.28 | 7 | |
| Pose Estimation | ACID (Avg) | Rotation Avg Error (°)4.568 | 7 | |
| Pose Estimation | ACID Medium | Rotation Avg Error (°)4.325 | 7 | |
| Rotation Estimation | sELP Small overlap (test) | MGE51.3 | 7 | |
| Pose Estimation | RealEstate-10K (Small) | Rotation Average Error (Avg)12.604 | 7 | |
| Pose Estimation | RealEstate-10K Medium | Rotation Average Error (Degrees)12.168 | 7 | |
| Pose Estimation | RealEstate-10K Large | Rotation Avg Error (°)12.771 | 7 | |
| Pose Estimation | RealEstate-10K (Avg) | Rotation Avg Error12.585 | 7 |
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