Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels
By Ali O. Ulusoy, Michael J. Black, Andreas Geiger. 2017
48 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Andreas Geiger
- Marc Pollefeys
- Yiyi Liao
- Luc Van Gool
- Ali O. Ulusoy
- Simon Donné
- Horst Bischof
- Pablo Speciale
In Computer Vision and Pattern Recognition
- Andreas Geiger
- Luc Van Gool
- Jitendra Malik
- Ming-Hsuan Yang
- Xiaogang Wang
- Dacheng Tao
- Andrea Vedaldi
- Marc Pollefeys
In Geology
- Andreas Geiger
- Marc Pollefeys
- Luc Van Gool
- Simon Donné
- Yiyi Liao
- Ali O. Ulusoy
- Silvano Galliani
- Pablo Speciale
Citations in the corpus
- RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials (2018, background): “[34] integrated scene specific 3D shape knowledge to further improve the quality of the 3D reconstructions.”
- OctNetFusion: Learning Depth Fusion from Data (2017, background): “[49, 48] consider a fully probabilistic model for image based 3D reconstruction.”
- 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].”
- Deep Marching Cubes: Learning Explicit Surface Representations (2018, background): “Unfortunately, the expressiveness of such prior models is limited to simple local smoothness assumptions [2, 17, 21, 47] or very specialized shape models [1, 15, 16, 42].”