Deep Shape from a Low Number of Silhouettes

By Xinhan Di, Rozenn Dahyot, Mukta Prasad. 2016

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bo Yang
2. Andrea Vedaldi
3. Maxim Tatarchenko
4. Jitendra Malik
5. Thomas Brox
6. Alexey Dosovitskiy
7. Andrew Markham
8. Ruslan Salakhutdinov

Citations in the corpus

• Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017, methodology): “Applications include single- and multi-view 3D object reconstruction trained in supervised [14, 5, 7, 17] and unsupervised [37, 12, 19] ways, probabilistic generative modeling of 3D shapes [36, 35, 29], semantic segmentation [2, 4] and shape deformation [39].”
• 3D Object Dense Reconstruction from a Single Depth View (2018, background): “Recently, [40] [41] [42] [43] [44] [45] leverage deep neural nets to learn the 3D shape from multiple RGB images.”
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, background): “Recently, [12] [42] [57] [54] [8] [6] [43] [49] [31] leverage deep neural nets to learn a 3D shape from multiple images.”