Abstract
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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Cordes, C., Ackermann, H., Rosenhahn, B. (2013). A Low-Rank Constraint for Parallel Stereo Cameras. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_4
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DOI: https://doi.org/10.1007/978-3-642-40602-7_4
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