Efficient Solution of Point-Line Absolute Pose
About
We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically, we address the two previously-studied minimal problems of estimating camera extrinsics from $p \in \{ 1, 2 \}$ point--point correspondences and $l=3-p$ line--line correspondences. To the best of our knowledge, all of the previously-known practical solutions to these problems required computing the roots of degree $\ge 4$ (univariate) polynomials when $p=2$, or degree $\ge 8$ polynomials when $p=1.$ We describe and implement two elementary solutions which reduce the degrees of the needed polynomials from $4$ to $2$ and from $8$ to $4$, respectively. We show experimentally that the resulting solvers are numerically stable and fast: when compared to the previous state-of-the art, we may obtain nearly an order of magnitude speedup. The code is available at \url{https://github.com/petrhruby97/efficient\_absolute}
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Absolute Pose Estimation | Oxford Multi-view Model House 1.0 | Timing (ms)7.72 | 12 | |
| Absolute Pose Estimation | Oxford Multi-view Corridor | Timing (ms)11.32 | 6 | |
| Absolute Pose Estimation | Oxford Multi-view (Merton II) | Timing (ms)26.7 | 6 | |
| Absolute Pose Estimation | Oxford Multi-view Merton III | Timing (ms)10.91 | 6 | |
| Absolute Pose Estimation | Oxford Multi-view (Library) | Timing (ms)10.04 | 6 | |
| Absolute Pose Estimation | Oxford Multi-view (Wadham) | Timing (ms)22.96 | 6 | |
| Camera pose estimation | Synthetic P2P1L noiseless | Mean Error313.8 | 4 | |
| Camera pose estimation | Synthetic P1P2L (noiseless) | Mean Error504 | 2 |