A Low-Rank Constraint for Parallel Stereo Cameras
Stereo-camera systems enjoy wide popularity since they provide more restrictive constraints for 3d-reconstruction. Given an image sequence taken by parallel stereo cameras, a low-rank constraint is derived on the measurement data. Correspondences between left and right images are not necessary yet reduce the number of optimization parameters. Conversely, traditional algorithms for stereo factorization require all feature points in both images to be matched, otherwise left and right image streams need be factorized independently. The performance of the proposed algorithm will be evaluated on synthetic data as well as two real image applications.
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- 1.Angst, R., Pollefeys, M.: Static multi-camera factorization using rigid motion. In: International Conference on Computer Vision (ICCV), pp. 1203–1210 (2009)Google Scholar
- 2.Bregler, C., Hertzmann, A., Biermann, H.: Recovering non-rigid 3d shape from image streams. In: IEEE Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC, USA, pp. 690–696 (2000)Google Scholar
- 6.Irani, M.: Multi-frame optical flow estimation using subspace constraints. In: International Conference on Computer Vision (ICCV), pp. 626–633 (1999)Google Scholar
- 8.Kanatani, K., Sugaya, Y.: Factorization without factorization: complete recipe. Tech. Rep. 1&2, Okayama University, Japan (March 2004)Google Scholar
- 13.Wold, H.: Estimation of principal components and related models by iterative least squares. In: Krishnaiah (ed.) Multivariate Analysis, pp. 391–420 (1966)Google Scholar