Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh

By Sudipta N. Sinha, Philippos Mordohai, Marc Pollefeys. 2007

177 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning a Multi-View Stereo Machine (2017, neutral): “In the multi-view setting, early work focused on using silhouette information via visual hulls [32], incorporating photo-consistency to deal with concavities (photo hull) [29], and shape reﬁnement using optimization [55, 50, 7, 15].”
Discrete optimization of ray potentials for semantic 3D reconstruction (2015, methodology): “In [23], the silhouettes are handled in a two-stage process, where the initial surface is reprojected into each image and the interior is heuristically corrected using the sets of erroneous pixels, by ﬁnding the most photo-consistent voxels along the ray.”
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint (2016, methodology): “This is done either in the optimization as a constraint, meaning that the reconstruction needs to be consistent with the image silhouettes [29, 13], or by deriving pervoxel occupancy probability [11].”
Accurate, Dense, and Robust Multi-View Stereopsis (2007, background): “They can be roughly classified into four classes according to the underlying object models: Voxel-based approaches [3], [4], [5], [6], [7], [8], [9] require knowing a bounding box that contains the scene, and their accuracy is limited by the resolution of the voxel grid.”
Multi-View Stereo: A Tutorial (2015, background): “MVS algorithms went through two major developments that took them to their current state: They left the laboratory setting to a small-scale outdoor scenes [174, 102, 85, 169, 190], e.”
Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo (2008, background): “Other works have shown the graph-cut formulation to perform well as a global optimisation stage [12, 21].”
PatchmatchNet: Learned Multi-View Patchmatch Stereo (2020, methodology): “Traditional MVS. Traditional MVS methods can be divided into four categories: voxel-based [34, 36], surface evolution based [14,25], patch-based [15,27] and depth map based [16,31,38].”
High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012, unknown): “This includes space carving [49], [37], [8], [68], level sets [14], [32],[46], and volumetric graph cuts [65], [6], [28], [43], [61] (though [50], [27] propose regular volumetric grid adaptive to photo-consistency measures to push the resolution limit further).”
Continuous Global Optimization in Multiview 3D Reconstruction (2007, methodology): “A possible remedy is to use an adaptive multi-resolution scheme (Sinha et al., 2007) or a sparse grid (Labatut et al., 2007), but these techniques can not give any guarantee about globality of the computed solution.”
Accurate, Dense, and Robust Multiview Stereopsis (2010, background): “They can be roughly classified into four classes according to the underlying object models: Voxel-based approaches [3], [4], [5], [6], [7], [8], [9] require knowing a bounding box that contains the scene, and their accuracy is limited by the resolution of the voxel grid.”