S. Agarwal and K. Mierle, Others: Ceres solver

S. Agarwal, N. Snavely, S. M. Seitz, and R. Szeliski, Bundle adjustment in the large, Proc. ECCV, pp.29-42, 2010.
DOI : 10.1007/978-3-642-15552-9_3

URL : http://grail.cs.washington.edu/pub/papers/agarwal2010bai.pdf

J. Albersmeyer and M. Diehl, The lifted newton method and its application in optimization, SIAM Journal on Optimization, vol.20, issue.3, pp.1655-1684, 2010.
DOI : 10.1137/080724885

M. J. Black and P. Anandan, The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields, Computer vision and image understanding, vol.63, issue.1, pp.75-104, 1996.
DOI : 10.1006/cviu.1996.0006

A. Blake and A. Zisserman, Visual reconstruction, 1987.

T. A. Davis and Y. Hu, The university of florida sparse matrix collection, ACM Transactions on Mathematical Software (TOMS), vol.38, issue.1, p.1, 2011.
DOI : 10.1145/2049662.2049663

URL : http://www.cise.ufl.edu/submit/files/file_298.pdf

D. M. Dunlavy and D. P. O'leary, Homotopy optimization methods for global optimization, 2005.

J. Engel, T. Schöps, and D. Cremers, LSD-SLAM: Large-scale direct monocular slam, European Conference on Computer Vision, pp.834-849, 2014.
DOI : 10.1007/978-3-319-10605-2_54

URL : http://vision.in.tum.de/_media/spezial/bib/engel14eccv.pdf

C. Engels, H. Stewénius, and D. Nistér, Bundle adjustment rules, Photogrammetric Computer Vision (PCV, 2006.

M. Fischler and R. Bolles, Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.
DOI : 10.1016/b978-0-08-051581-6.50070-2

URL : http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA460585&Location=U2&doc=GetTRDoc.pdf

D. Geman and G. Reynolds, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Mach. Intell, vol.14, issue.3, pp.367-383, 1992.
DOI : 10.1109/34.120331

D. Geman and C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995.
DOI : 10.1109/83.392335

URL : http://www.math.umass.edu/~geman/Papers/nonlinear.ps.gz

P. J. Green, Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives, Journal of the Royal Statistical Society. Series B (Methodological), pp.149-192, 1984.

J. H. Hong and A. Fitzgibbon, Secrets of matrix factorization: Approximations, numerics, manifold optimization and random restarts, Proceedings of the IEEE International Conference on Computer Vision, pp.4130-4138, 2015.
DOI : 10.1109/iccv.2015.470

P. J. Huber, Robust statistics, 1981.
DOI : 10.1002/0471725250

URL : https://onlinelibrary.wiley.com/doi/pdf/10.1002/0471725250.fmatter

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, science, vol.220, issue.4598, pp.671-680, 1983.

K. Lange, D. R. Hunter, and I. Yang, Optimization transfer using surrogate objective functions, Journal of computational and graphical statistics, vol.9, issue.1, pp.1-20, 2000.

S. Liwicki, C. Zach, O. Miksik, and P. H. Torr, Coarse-to-fine planar regularization for dense monocular depth estimation, European Conference on Computer Vision, pp.458-474, 2016.

H. Mobahi and J. W. Fisher, On the link between gaussian homotopy continuation and convex envelopes, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.43-56, 2015.

H. Mobahi, I. Fisher, and J. W. , A theoretical analysis of optimization by gaussian continuation, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

K. Rose, Deterministic annealing for clustering, compression, classification, regression, and related optimization problems, Proceedings of the IEEE, vol.86, issue.11, pp.2210-2239, 1998.

P. H. Torr and A. Zisserman, MLESAC: A new robust estimator with application to estimating image geometry, Computer Vision and Image Understanding, vol.78, issue.1, pp.138-156, 2000.

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, Bundle adjustment-A modern synthesis, Vision Algorithms: Theory and Practice, vol.1883, pp.298-372, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00548290

M. Ye, R. M. Haralick, and L. G. Shapiro, Estimating piecewise-smooth optical flow with global matching and graduated optimization, vol.25, pp.1625-1630, 2003.

Y. Yuan, A review of trust region algorithms for optimization, ICM99: Proceedings of the Fourth International Congress on Industrial and Applied Mathematics, 1999.

C. Zach and G. Bourmaud, Iterated lifting for robust cost optimization, Proc. BMVC, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01718012

C. Zach and G. Bourmaud, Descending, lifting or smoothing: Secrets of robust cost optimization (supplementary material), Proc. ECCV, 2018.

C. Zach and G. Bourmaud, Multiplicative vs. additive half-quadratic minimization for robust cost optimization, Proc. BMVC, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01875291

C. Zach, Robust bundle adjustment revisited, Proc. ECCV, pp.772-787, 2014.

Q. Y. Zhou, J. Park, and V. Koltun, Fast global registration, Proc. ECCV, pp.766-782, 2016.

M. Zollhöfer, M. Nießner, S. Izadi, C. Rehmann, C. Zach et al., Real-time non-rigid reconstruction using an RGB-D camera, 2014.