Name
Abstract | ||
|---|---|---|
Determining relative camera pose is a fundamental problem in computer vision, and pose is often computed from feature correspondences. For point features, a minimum of five correspondences are required to determine the pose between two calibrated cameras, and eight corresponding points can be used to form a linear solution. However, most feature detectors used in practice produce points with an associated orientation. This work demonstrates that with oriented features the relative pose of two cameras can be computed from just four point correspondences, or seven with a linear solution. These new four- and seven-point algorithms do not require any additional sensors or parameters, but exploit information (feature orientation) that is already computed by most existing structure-from-motion systems. On the DTU multi-view stereo data set the four-point algorithm is shown to be 55% faster than the five-point algorithm, and the seven-point linear algorithm gives a 43% speed improvement over the eight-point algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/3DV.2018.00034 | 2018 International Conference on 3D Vision (3DV) |
Keywords | Field | DocType |
Relative camera pose,essential matrix estimation,oriented features | Kernel (linear algebra),Computer vision,Feature orientation,Feature detection,Computer science,Linear algorithm,Exploit,Pose,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2378-3826 | 978-1-5386-8426-9 | 0 |
PageRank | References | Authors |
0.34 | 23 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Mills | 1 | 41 | 17.74 |