Abstract
The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).
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References
Baumgart, B. (1974). Geometric modeling for computer vision. Ph.D. thesis, Stanford University.
Bouguet, J. Y. (2008). Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc.
Courchay, J. (2007). Auto-calibration á partir d’une séquence d’images (MVA internship report).
DXO (2008). DxO Labs. DxO Optics Pro (http://www.dxo.com).
Fischler, M., & Bolles, R. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. CACM 24(6).
Franco, J. B., & Boyer, E. (2003). Exact polyhedral visual hulls. In BMVC.
Fua, P. (2000). Regularized bundle-adjustment to model heads from image sequences without calibration data. International Journal of Computer Vision, 38(2), 153–171. doi:10.1023/A:1008105802790.
Furukawa, Y., & Ponce, J. (2006). Carved visual hulls for image-based modeling. In ECCV (pp. 564–577).
Furukawa, Y., & Ponce, J. (2007). Accurate, dense, and robust multi-view stereopsis. In CVPR.
Furukawa, Y., & Ponce, J. (2008a). Accurate camera calibration from multi-view stereo and bundle adjustment. In CVPR.
Furukawa, Y., & Ponce, J. (2008b). PBA. http://www.cs.washington.edu/homes/furukawa/research/pba.
Furukawa, Y., & Ponce, J. (2008c). PMVS. http://www.cs.washington.edu/homes/furukawa/research/pmvs.
Hartley, R. I., & Zisserman, A. (2004). Multiple view geometry in computer vision. Cambridge: Cambridge University Press.
Hernández Esteban, C., & Schmitt, F. (2004). Silhouette and stereo fusion for 3D object modeling. CVIU 96(3).
Hernández Esteban, C., Schmitt, F., & Cipolla, R. (2007). Silhouette coherence for camera calibration under circular motion. PAMI 29.
Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. In Symp. Geom. Proc.
Lavest, J. M., Viala, M., & Dhome, M. (1998). Do we really need an accurate calibration pattern to achieve a reliable camera calibration? In ECCV.
Lourakis, M., & Argyros, A. (2008). SBA: A generic sparse bundle adjustment C/C++ package based on the Levenberg-Marquardt algorithm. http://www.ics.forth.gr/~lourakis/sba/.
Martinec, D., & Pajdla, T. (2007). Robust rotation and translation estimation in multiview reconstruction. In CVPR (pp. 1–8).
Nister, D. (2004). An efficient solution to the five-point relative pose problem. PAMI 26(6).
Pollefeys, M., Gool, L. V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., & Koch, R. (2004). Visual modeling with a hand-held camera. International Journal of Computer Vision, 59(3), 207–232. doi:10.1023/B:VISI.0000025798.50602.3a.
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR.
Sinha, S., & Pollefeys, M. (2005). Multi-view reconstruction using photo-consistency and exact silhouette constraints: A maximum-flow formulation. In ICCV.
Tran, S., & Davis, L. (2006). 3d surface reconstruction using graph cuts with surface constraints. In ECCV.
Triggs, B., McLauchlan, P., Hartley, R., & Fitzgibbon, A. (2000). Bundle adjustment—A modern synthesis. In W. Triggs, A. Zisserman, & R. Szeliski (Eds.). Vision algorithms: theory and practice (pp. 298–375). Berlin: Springer.
Triggs, W. (1997). Auto-calibration and the absolute quadric. In CVPR.
Tsai, R. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras. Robotics and Automation 3(4).
Uffenkamp, V. (1993). State of the art of high precision industrial photogrammetry. In Third International Workshop on Accelerator Alignment, Annecy, France.
Wong, K. K., & Cipolla, R. (2004). Reconstruction of sculpture from its profiles with unknown camera positions. IEEE Transactions on Image Processing.
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Furukawa, Y., Ponce, J. Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment. Int J Comput Vis 84, 257–268 (2009). https://doi.org/10.1007/s11263-009-0232-2
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DOI: https://doi.org/10.1007/s11263-009-0232-2