Shape from shading
International Journal of Computer Vision
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Integration of Multiview Stereo and Silhouettes Via Convex Functionals on Convex Domains
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Continuous Global Optimization in Multiview 3D Reconstruction
International Journal of Computer Vision
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
IEEE Transactions on Image Processing
International Journal of Computer Vision
Scale robust multi view stereo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
A Super-Resolution Framework for High-Accuracy Multiview Reconstruction
International Journal of Computer Vision
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In this work the weighted minimal surface model traditionally used in multiview stereo is revisited. We propose to generalize the classical photoconsistency-weighted minimal surface approach by means of an anisotropic metric which allows to integrate a specified surface orientation into the optimization process. In contrast to the conventional isotropic case, where all spatial directions are treated equally, the anisotropic metric adaptively weights the regularization along different directions so as to favor certain surface orientations over others. We show that the proposed generalization preserves all properties and globality guarantees of continuous convex relaxation methods. We make use of a recently introduced efficient primal-dual algorithm to solve the arising saddle point problem. In multiple experiments on real image sequences we demonstrate that the proposed anisotropic generalization allows to overcome oversmoothing of small-scale surface details, giving rise to more precise reconstructions.