Continuous Global Optimization in Multiview 3D Reconstruction

By Kalin S. Kolev, Maria Klodt, Thomas Brox, Daniel Cremers. 2007

185 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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Citations in the corpus

Hierarchical Surface Prediction for 3D Object Reconstruction (2017, methodology): “The common methods can be broadly grouped as implicit volumetric reconstruction [7], [18], [19], [22], [38] or explicit mesh based [9], [12] approaches.”
Generalized Connectivity Constraints for Spatio-temporal 3D Reconstruction (2014, unknown): “[19] to the temporal domain.”
Photometric Bundle Adjustment for Dense Multi-view 3D Modeling (2014, methodology): “For example [17] uses the Level Set framework using a global image score, [10] uses a convex formulation minimizing a photometric error defined over a discretized grid.”
A Symmetry Prior for Convex Variational 3D Reconstruction (2016, methodology): “…into the variational volumetric 3D reconstruction framework which has been used in various settings, e.g. depth map fusion [29], 3D reconstruction [9,27], multi-label semantic reconstruction [5], and spatio-temporal reconstruction [17], with anisotropic regularization [10,5,18] or connectivity…”
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration (2007, methodology): “A commonly used approach is to penalize the surface area of the resulting 3D model, which has been successfully applied in other domains in conjunction with graph-cut algorithms [31] [16] [5] and variational techniques (e.g. [34] [22] [24] [21]).”
Variable Selection (2019, methodology): “Using convex relaxation techniques, this allows to solve certain classes of weighted minimal surface problems, which, for instance, arise in segmentation [12] and 3D reconstruction [10], in a globally optimal manner.”
High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012, unknown): “Many dense multiview methods rely on this information either as an initial guess for further optimization [26], [18], [29], [28], [55], [61], [64], [69], as a soft constraint [26], [35], or even as a hard constraint [51], [18] to be fulfilled by the reconstructed shape.”
Continuous depth estimation for multi-view stereo (2009, methodology): “In MVS area, Kolev [14] proposed a continuous global optimization algorithm parallel to discrete volumetric graph cuts [28].”