ShapeCodes: Self-supervised Feature Learning by Lifting Views to Viewgrids
By Dinesh Jayaraman, Ruohan Gao, Kristen Grauman. 2017
15 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, 3D Vision & Pose
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Jiajun Wu
- Josh Tenenbaum
- Zhoutong Zhang
- Chengkai Zhang
- Xiuming Zhang
- Bill Freeman
- Kristen Grauman
- Santhosh K. Ramakrishnan
In Computer Vision and Pattern Recognition
- Jiajun Wu
- Jitendra Malik
- Kristen Grauman
- Josh Tenenbaum
- Andrea Vedaldi
- Luc Van Gool
- Ming-Hsuan Yang
- Trevor Darrell
In Representation & Self-Supervised Learning
- Jiajun Wu
- Kristen Grauman
- Santhosh K. Ramakrishnan
- Ziad Al-Halah
- Josh Tenenbaum
- Chengkai Zhang
- Bill Freeman
- Antonio Torralba
In 3D Vision & Pose
- Jiajun Wu
- Josh Tenenbaum
- Chengkai Zhang
- Zhoutong Zhang
- Bill Freeman
- Xiuming Zhang
- Santhosh K. Ramakrishnan
- Ziad Al-Halah
Citations in the corpus
- Learning to Reconstruct Shapes from Unseen Classes (2018, methodology): “Another way of representing 3D shapes is to use a set of multi-view depth images [Soltani et al., 2017, Shin et al., 2018, Jayaraman et al., 2018].”
- Occupancy Anticipation for Efficient Exploration and Navigation (2020, methodology): “While some methods allow pixelwise extrapolation outside the current field of view (FoV) [51,68,78,27], they do not permit inferences about occluded regions in the scene.”