Multi-View Stereo Revisited

By Michael Goesele, Brian Curless, S. M. Seitz. Computer Vision and Pattern Recognition, 2006

415 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bb5bf59928528869f82e8522d15f77c22a753b29

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo (2008, methodology): “The works of [7, 6] have used derivatives of this technique with slight modifications, for example the inclusion of a Parzen window to filter the consensus matches in [6].”
Accurate, Dense, and Robust Multiview Stereopsis (2010, background): “Approaches based on multiple depth maps [14], [15], [16] are more flexible, but require fusing individual depth maps into a single 3D model.”
Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts (2007, background): “outdor architectural scenes) are those representing geometry by several depth maps [18, 17, 23, 37, 38, 39].”
Multi-View Stereo: A Tutorial (2015, methodology): “Reconstruction results of a winner-takes-all depthmap algorithm with a robust photo-consistency function by Goesele, Curless, and Seitz [80] are given in Figure 3.”
Large-Scale Data for Multiple-View Stereopsis (2016, methodology): “Most successful MVS algorithms can be divided into two main categories: point-cloud-based methods (e.g. Campbell et al. 2008; Tola et al. 2012; Goesele et al. 2006; Hiep et al. 2009; Furukawa and Ponce 2010; Vogiatzis et al. 2007) and volume-based methods (e.g. Liu and Cooper 2011; Kolev et al.…”
Multi-View Stereo for Community Photo Collections (2007, methodology): “A number of recent stereo matching methods have used outlier rejection techniques to identify occlusions in the matching step [4, 6].”
Continuous depth estimation for multi-view stereo (2009, methodology): “In recent years, Goesele [ 10 ] revisits depth map based approaches using pixel by pixel window matching technique to retrieve the high fidelity matching pixels and merge the resulted depth maps using volumetric meshing algorithms.”
Accurate, Dense, and Robust Multi-View Stereopsis (2007, background): “Approaches based on multiple depth maps [14], [15], [16] are more flexible, but require fusing individual depth maps into a single 3D model.”
Detailed Real-Time Urban 3D Reconstruction from Video (2007, background): “[24] presented a two-stage algorithm which merges depth maps produced by a simple algorithm.”
Global Optimization for Shape Fitting (2007, methodology): “To test this, we considered incomplete meshes produced by mutltiview reconstruction method [ 8 ] on Middlebury multiview stereo page datasets [17] kindly provided by the authors of [8].”
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration (2007, background): “The concavity visible in the second view on the mesh (Figure 3(d)) indicates a challenging region, where many multiview stereo methods have difficulties (e.g. [13] [29] [28]).”
Real-Time Visibility-Based Fusion of Depth Maps (2007, background): “[6] presented a two-stage approach which merges depth maps produced by a simple algorithm.”
High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012, neutral): “The performance of some depth maps-based methods [36], [58], [56], [22], [57], [23], [24] for complete reconstruction, however, seems to be lower than previously discussed approaches, as regards either accuracy or completeness of the obtained model.”
Large Scale Multi-view Stereopsis Evaluation (2014, neutral): “Feature expansion [3] and depthmap fusion [2, 5, 7, 19, 20].”
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms (2006, result): “[63] compute a depth map from each camera viewpoint (similar to [31]) and merge the results using VRIP [62].”