Name
Abstract | ||
|---|---|---|
We present an autocalibration algorithm for upgrading a projective reconstruction to a metric reconstruction by estimating the absolute dual quadric. The algorithm enforces the rank degeneracy and the positive semidefiniteness of the dual quadric as part of the estimation procedure, rather than as a post-processing step. Furthermore, the method allows the user, if he or she so desires, to enforce conditions on the plane at infinity so that the reconstruction satisfies the chirality constraints.The algorithm works by constructing low degree polynomial optimization problems, which are solved to their global optimum using a series of convex linear matrix inequality relaxations. The algorithm is fast, stable, robust and has time complexity independent of the number of views. We show extensive results on synthetic as well as real datasets to validate our algorithm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383067 | 2007 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-8 |
Keywords | Field | DocType |
calibration,computer vision,satisfiability,robustness,layout,polynomials,time complexity,linear matrix inequality,image reconstruction,optimization problem | Plane at infinity,Polynomial,Computer science,Artificial intelligence,Time complexity,Optimization problem,Linear matrix inequality,Quadric,Iterative reconstruction,Mathematical optimization,Pattern recognition,Degree of a polynomial,Algorithm | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 |
Citations | PageRank | References |
19 | 0.65 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manmohan Chandraker | 1 | 451 | 25.58 |
| Sameer Agarwal | 2 | 10328 | 478.10 |
| Fredrik Kahl | 3 | 1415 | 92.61 |
| David Nistér | 4 | 2265 | 118.02 |
| David Kriegman | 5 | 7693 | 451.96 |