Name
Abstract | ||
|---|---|---|
It is important to estimate accurate camera parameters in multi-view stereo. In this paper, we use three-view relations, the trifocal tensor, to improve the Bundler, a popular structure from motion (SfM) system, for estimating accurate camera parameters. We propose a novel method: the Robust Orthogonal Particle Swarm Optimization (ROPSO) to estimate a robust and accurate trifocal tensor. In ROPSO, we formulate the trifocal tensor estimation as a global optimization problem and use the particle swarm optimization (PSO) for parameter searching. The orthogonal array is used to select the representative initial particles in PSO for more stable results. In the experiments, we use simulated and real ground truth data for statistical analysis. The experimental results show that the proposed ROPSO can achieve more accurate estimation of the trifocal tensor than the traditional methods and has higher probability to find the optimization solution than the traditional methods. Based on the trifocal tensor estimated by the proposed method, the SfM estimation errors can effectively be reduced. The average reprojection errors are reduced from 21.5 pixels to less than 1 pixel. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.patrec.2012.12.023 | Pattern Recognition Letters |
Keywords | Field | DocType |
particle swarm optimization,traditional method,optimization solution,trifocal tensor estimation,accurate camera parameter,global optimization problem,motion estimation,trifocal tensor,robust trifocal tensor constraint,accurate trifocal tensor,accurate estimation,sfm estimation error,structure from motion,orthogonal array | Structure from motion,Particle swarm optimization,Reprojection error,Computer vision,Tensor,Pixel,Artificial intelligence,Motion estimation,Estimation theory,Mathematics,Trifocal tensor | Journal |
Volume | Issue | ISSN |
34 | 6 | 0167-8655 |
Citations | PageRank | References |
0 | 0.34 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai-Hsuan Chan | 1 | 10 | 3.23 |
| Cheng-Yuan Tang | 2 | 49 | 10.61 |
| Maw-kae Hor | 3 | 47 | 8.29 |
| Yi-Leh Wu | 4 | 407 | 71.70 |